From fa228dac966dea496306dd05aab654b0f12eee8f Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 03:31:19 -0400 Subject: [PATCH 01/46] Removed auto generated API files --- .gitignore | 2 + .../src.MEDS_tabular_automl.configs.rst | 30 --------------- ..._tabular_automl.configs.tabularization.rst | 4 -- ...src.MEDS_tabular_automl.describe_codes.rst | 23 ----------- .../src.MEDS_tabular_automl.file_name.rst | 18 --------- ...abular_automl.generate_static_features.rst | 19 ---------- ...abular_automl.generate_summarized_reps.rst | 20 ---------- ...DS_tabular_automl.generate_ts_features.rst | 20 ---------- .../src.MEDS_tabular_automl.mapper.rst | 18 --------- .../generated/src.MEDS_tabular_automl.rst | 38 ------------------- ...MEDS_tabular_automl.scripts.cache_task.rst | 17 --------- ..._tabular_automl.scripts.describe_codes.rst | 16 -------- ..._tabular_automl.scripts.launch_xgboost.rst | 27 ------------- .../src.MEDS_tabular_automl.scripts.rst | 34 ----------------- ...bular_automl.scripts.tabularize_static.rst | 16 -------- ..._automl.scripts.tabularize_time_series.rst | 16 -------- .../src.MEDS_tabular_automl.utils.rst | 37 ------------------ docs/source/generated/src.rst | 30 --------------- 18 files changed, 2 insertions(+), 383 deletions(-) delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.configs.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.configs.tabularization.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.file_name.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.generate_static_features.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.generate_summarized_reps.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.generate_ts_features.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.mapper.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.scripts.cache_task.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.scripts.describe_codes.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.scripts.launch_xgboost.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.scripts.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_static.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_time_series.rst delete mode 100644 docs/source/generated/src.MEDS_tabular_automl.utils.rst delete mode 100644 docs/source/generated/src.rst diff --git a/.gitignore b/.gitignore index 68bc17f..57eed8c 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,5 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +docs/source/generated diff --git a/docs/source/generated/src.MEDS_tabular_automl.configs.rst b/docs/source/generated/src.MEDS_tabular_automl.configs.rst deleted file mode 100644 index ea0b463..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.configs.rst +++ /dev/null @@ -1,30 +0,0 @@ -src.MEDS\_tabular\_automl.configs -================================= - -.. automodule:: src.MEDS_tabular_automl.configs - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - src.MEDS_tabular_automl.configs.tabularization diff --git a/docs/source/generated/src.MEDS_tabular_automl.configs.tabularization.rst b/docs/source/generated/src.MEDS_tabular_automl.configs.tabularization.rst deleted file mode 100644 index 060282a..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.configs.tabularization.rst +++ /dev/null @@ -1,4 +0,0 @@ -src.MEDS\_tabular\_automl.configs.tabularization -================================================ - -.. automodule:: src.MEDS_tabular_automl.configs.tabularization diff --git a/docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst b/docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst deleted file mode 100644 index cbf802b..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.describe_codes.rst +++ /dev/null @@ -1,23 +0,0 @@ -src.MEDS\_tabular\_automl.describe\_codes -========================================= - -.. automodule:: src.MEDS_tabular_automl.describe_codes - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - clear_code_aggregation_suffix - compute_feature_frequencies - convert_to_df - convert_to_freq_dict - filter_parquet - filter_to_codes - get_feature_columns - get_feature_freqs diff --git a/docs/source/generated/src.MEDS_tabular_automl.file_name.rst b/docs/source/generated/src.MEDS_tabular_automl.file_name.rst deleted file mode 100644 index 3b75288..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.file_name.rst +++ /dev/null @@ -1,18 +0,0 @@ -src.MEDS\_tabular\_automl.file\_name -==================================== - -.. automodule:: src.MEDS_tabular_automl.file_name - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - get_model_files - get_task_specific_path - list_subdir_files diff --git a/docs/source/generated/src.MEDS_tabular_automl.generate_static_features.rst b/docs/source/generated/src.MEDS_tabular_automl.generate_static_features.rst deleted file mode 100644 index 7656f7f..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.generate_static_features.rst +++ /dev/null @@ -1,19 +0,0 @@ -src.MEDS\_tabular\_automl.generate\_static\_features -==================================================== - -.. automodule:: src.MEDS_tabular_automl.generate_static_features - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - convert_to_matrix - get_flat_static_rep - get_sparse_static_rep - summarize_static_measurements diff --git a/docs/source/generated/src.MEDS_tabular_automl.generate_summarized_reps.rst b/docs/source/generated/src.MEDS_tabular_automl.generate_summarized_reps.rst deleted file mode 100644 index b9f44d7..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.generate_summarized_reps.rst +++ /dev/null @@ -1,20 +0,0 @@ -src.MEDS\_tabular\_automl.generate\_summarized\_reps -==================================================== - -.. automodule:: src.MEDS_tabular_automl.generate_summarized_reps - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - aggregate_matrix - compute_agg - generate_summary - get_rolling_window_indicies - sparse_aggregate diff --git a/docs/source/generated/src.MEDS_tabular_automl.generate_ts_features.rst b/docs/source/generated/src.MEDS_tabular_automl.generate_ts_features.rst deleted file mode 100644 index e4fdd21..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.generate_ts_features.rst +++ /dev/null @@ -1,20 +0,0 @@ -src.MEDS\_tabular\_automl.generate\_ts\_features -================================================ - -.. automodule:: src.MEDS_tabular_automl.generate_ts_features - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - feature_name_to_code - get_flat_ts_rep - get_long_code_df - get_long_value_df - summarize_dynamic_measurements diff --git a/docs/source/generated/src.MEDS_tabular_automl.mapper.rst b/docs/source/generated/src.MEDS_tabular_automl.mapper.rst deleted file mode 100644 index a0a05ea..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.mapper.rst +++ /dev/null @@ -1,18 +0,0 @@ -src.MEDS\_tabular\_automl.mapper -================================ - -.. automodule:: src.MEDS_tabular_automl.mapper - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - get_earliest_lock - register_lock - wrap diff --git a/docs/source/generated/src.MEDS_tabular_automl.rst b/docs/source/generated/src.MEDS_tabular_automl.rst deleted file mode 100644 index 2fd4a60..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.rst +++ /dev/null @@ -1,38 +0,0 @@ -src.MEDS\_tabular\_automl -========================= - -.. automodule:: src.MEDS_tabular_automl - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - src.MEDS_tabular_automl.configs - src.MEDS_tabular_automl.describe_codes - src.MEDS_tabular_automl.file_name - src.MEDS_tabular_automl.generate_static_features - src.MEDS_tabular_automl.generate_summarized_reps - src.MEDS_tabular_automl.generate_ts_features - src.MEDS_tabular_automl.mapper - src.MEDS_tabular_automl.scripts - src.MEDS_tabular_automl.utils diff --git a/docs/source/generated/src.MEDS_tabular_automl.scripts.cache_task.rst b/docs/source/generated/src.MEDS_tabular_automl.scripts.cache_task.rst deleted file mode 100644 index 76d5c8c..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.scripts.cache_task.rst +++ /dev/null @@ -1,17 +0,0 @@ -src.MEDS\_tabular\_automl.scripts.cache\_task -============================================= - -.. automodule:: src.MEDS_tabular_automl.scripts.cache_task - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - generate_row_cached_matrix - main diff --git a/docs/source/generated/src.MEDS_tabular_automl.scripts.describe_codes.rst b/docs/source/generated/src.MEDS_tabular_automl.scripts.describe_codes.rst deleted file mode 100644 index 7b604e6..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.scripts.describe_codes.rst +++ /dev/null @@ -1,16 +0,0 @@ -src.MEDS\_tabular\_automl.scripts.describe\_codes -================================================= - -.. automodule:: src.MEDS_tabular_automl.scripts.describe_codes - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - main diff --git a/docs/source/generated/src.MEDS_tabular_automl.scripts.launch_xgboost.rst b/docs/source/generated/src.MEDS_tabular_automl.scripts.launch_xgboost.rst deleted file mode 100644 index 61afb61..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.scripts.launch_xgboost.rst +++ /dev/null @@ -1,27 +0,0 @@ -src.MEDS\_tabular\_automl.scripts.launch\_xgboost -================================================= - -.. automodule:: src.MEDS_tabular_automl.scripts.launch_xgboost - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - main - - - - - - .. rubric:: Classes - - .. autosummary:: - - Iterator - XGBoostModel diff --git a/docs/source/generated/src.MEDS_tabular_automl.scripts.rst b/docs/source/generated/src.MEDS_tabular_automl.scripts.rst deleted file mode 100644 index 15ca299..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.scripts.rst +++ /dev/null @@ -1,34 +0,0 @@ -src.MEDS\_tabular\_automl.scripts -================================= - -.. automodule:: src.MEDS_tabular_automl.scripts - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - src.MEDS_tabular_automl.scripts.cache_task - src.MEDS_tabular_automl.scripts.describe_codes - src.MEDS_tabular_automl.scripts.launch_xgboost - src.MEDS_tabular_automl.scripts.tabularize_static - src.MEDS_tabular_automl.scripts.tabularize_time_series diff --git a/docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_static.rst b/docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_static.rst deleted file mode 100644 index 61852e0..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_static.rst +++ /dev/null @@ -1,16 +0,0 @@ -src.MEDS\_tabular\_automl.scripts.tabularize\_static -==================================================== - -.. automodule:: src.MEDS_tabular_automl.scripts.tabularize_static - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - main diff --git a/docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_time_series.rst b/docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_time_series.rst deleted file mode 100644 index 066a968..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.scripts.tabularize_time_series.rst +++ /dev/null @@ -1,16 +0,0 @@ -src.MEDS\_tabular\_automl.scripts.tabularize\_time\_series -========================================================== - -.. automodule:: src.MEDS_tabular_automl.scripts.tabularize_time_series - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - main diff --git a/docs/source/generated/src.MEDS_tabular_automl.utils.rst b/docs/source/generated/src.MEDS_tabular_automl.utils.rst deleted file mode 100644 index ba45d9c..0000000 --- a/docs/source/generated/src.MEDS_tabular_automl.utils.rst +++ /dev/null @@ -1,37 +0,0 @@ -src.MEDS\_tabular\_automl.utils -=============================== - -.. automodule:: src.MEDS_tabular_automl.utils - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - add_static_missing_cols - array_to_sparse_matrix - get_events_df - get_feature_indices - get_feature_names - get_flat_rep_feature_cols - get_min_dtype - get_prediction_ts_cols - get_shard_prefix - get_static_col_dtype - get_static_feature_cols - get_ts_feature_cols - get_unique_time_events_df - hydra_loguru_init - load_matrix - load_meds_data - load_tqdm - parse_static_feature_column - sparse_matrix_to_array - store_config_yaml - store_matrix - write_df diff --git a/docs/source/generated/src.rst b/docs/source/generated/src.rst deleted file mode 100644 index b9f0096..0000000 --- a/docs/source/generated/src.rst +++ /dev/null @@ -1,30 +0,0 @@ -src -=== - -.. automodule:: src - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - src.MEDS_tabular_automl From 27cba9bc2c3ce6e7ace5d1b7dbf9cc301f0a4d3c Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 03:36:06 -0400 Subject: [PATCH 02/46] Small README modifications --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9575b17..1ce978f 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ This repository consists of two key pieces: what is more advanced is the efficient construction, storage, and loading of tabular features for the candidate AutoML models, enabling a far more extensive search over different featurization strategies. -### Scripts and Examples +## Scripts and Examples See `tests/test_integration.py` for an example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. @@ -54,7 +54,7 @@ script is a functional test that is also run with `pytest` to verify the correct For an end to end example over MIMIC-IV, see the [companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV) For an end to end example over Philips eICU, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). -### Core CLI Scripts Overview +## Core CLI Scripts Overview 1. **`meds-tab-describe`**: This command processes MEDS data shards to compute the frequencies of different code-types @@ -124,7 +124,7 @@ For an end to end example over Philips eICU, see the [eICU companion repository] 6. **`meds-tab-xgboost-sweep`**: Conducts an Optuna hyperparameter sweep to optimize over `window_sizes`, `aggregations`, and `min_code_inclusion_frequency`, aiming to enhance model performance and adaptability. -### Additional CLI Scripts +## Additional CLI Scripts 1. **`generate-permutations`**: Generates and prints a sorted list of all permutations from a comma separated input. This is provided for the convenience of sweeping over all possible combinations of window sizes and aggregations. @@ -149,7 +149,7 @@ For an end to end example over Philips eICU, see the [eICU companion repository] # How does MEDS-Tab Work? -#### What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? +## What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? This is a common misconception. _Tabular_ data refers to data that can be organized in a consistent, logical set of rows/columns such that the entirety of a "sample" or "instance" for modeling or analysis is contained From adf93b0d30fa52f721edbe8506ed02530a8a7177 Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 03:47:20 -0400 Subject: [PATCH 03/46] Use relative imports inside the (now nested) scripts files. --- docs/source/conf.py | 19 ++++++++++++++----- src/MEDS_tabular_automl/scripts/cache_task.py | 6 +++--- .../scripts/describe_codes.py | 13 ++++--------- .../scripts/launch_xgboost.py | 6 +++--- .../scripts/tabularize_static.py | 10 +++++----- .../scripts/tabularize_time_series.py | 12 ++++++------ src/MEDS_tabular_automl/utils.py | 2 +- 7 files changed, 36 insertions(+), 32 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 34fb932..1f90468 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -10,15 +10,24 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = "MEDS-Tab" -copyright = "2024, Matthew McDermott, Nassim Oufattole, Teya Bergamaschi" -author = "Matthew McDermott, Nassim Oufattole, Teya Bergamaschi" -release = "0.0.1" -version = "0.0.1" +copyright = "2024, Nassim Oufattole, Matthew McDermott, Teya Bergamaschi, Aleksia Kolo, Hyewon Jeong" +author = "Nassim Oufattole, Matthew McDermott, Teya Bergamaschi, Aleksia Kolo, Hyewon Jeong" +release = "0.0.2" +version = "0.0.2" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -sys.path.insert(0, os.path.abspath("../..")) +# -- Path setup +from pathlib import Path + +__location__ = Path(os.path.dirname(__file__)) +__src__ = __location__ / "../.." + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +sys.path.insert(0, str(__src__)) extensions = [ "sphinx.ext.duration", diff --git a/src/MEDS_tabular_automl/scripts/cache_task.py b/src/MEDS_tabular_automl/scripts/cache_task.py index b42e765..62df9b8 100644 --- a/src/MEDS_tabular_automl/scripts/cache_task.py +++ b/src/MEDS_tabular_automl/scripts/cache_task.py @@ -10,9 +10,9 @@ import scipy.sparse as sp from omegaconf import DictConfig -from MEDS_tabular_automl.file_name import list_subdir_files -from MEDS_tabular_automl.mapper import wrap as rwlock_wrap -from MEDS_tabular_automl.utils import ( +from ..file_name import list_subdir_files +from ..mapper import wrap as rwlock_wrap +from ..utils import ( CODE_AGGREGATIONS, STATIC_CODE_AGGREGATION, STATIC_VALUE_AGGREGATION, diff --git a/src/MEDS_tabular_automl/scripts/describe_codes.py b/src/MEDS_tabular_automl/scripts/describe_codes.py index 408980f..fdee111 100644 --- a/src/MEDS_tabular_automl/scripts/describe_codes.py +++ b/src/MEDS_tabular_automl/scripts/describe_codes.py @@ -10,19 +10,14 @@ from loguru import logger from omegaconf import DictConfig, OmegaConf -from MEDS_tabular_automl.describe_codes import ( +from ..describe_codes import ( compute_feature_frequencies, convert_to_df, convert_to_freq_dict, ) -from MEDS_tabular_automl.file_name import list_subdir_files -from MEDS_tabular_automl.mapper import wrap as rwlock_wrap -from MEDS_tabular_automl.utils import ( - get_shard_prefix, - hydra_loguru_init, - load_tqdm, - write_df, -) +from ..file_name import list_subdir_files +from ..mapper import wrap as rwlock_wrap +from ..utils import get_shard_prefix, hydra_loguru_init, load_tqdm, write_df config_yaml = files("MEDS_tabular_automl").joinpath("configs/describe_codes.yaml") if not config_yaml.is_file(): diff --git a/src/MEDS_tabular_automl/scripts/launch_xgboost.py b/src/MEDS_tabular_automl/scripts/launch_xgboost.py index 9089bd5..46233c2 100644 --- a/src/MEDS_tabular_automl/scripts/launch_xgboost.py +++ b/src/MEDS_tabular_automl/scripts/launch_xgboost.py @@ -13,9 +13,9 @@ from omegaconf import DictConfig, OmegaConf from sklearn.metrics import roc_auc_score -from MEDS_tabular_automl.describe_codes import get_feature_columns -from MEDS_tabular_automl.file_name import get_model_files, list_subdir_files -from MEDS_tabular_automl.utils import get_feature_indices, hydra_loguru_init +from ..describe_codes import get_feature_columns +from ..file_name import get_model_files, list_subdir_files +from ..utils import get_feature_indices, hydra_loguru_init config_yaml = files("MEDS_tabular_automl").joinpath("configs/launch_xgboost.yaml") if not config_yaml.is_file(): diff --git a/src/MEDS_tabular_automl/scripts/tabularize_static.py b/src/MEDS_tabular_automl/scripts/tabularize_static.py index d7434f0..2474442 100644 --- a/src/MEDS_tabular_automl/scripts/tabularize_static.py +++ b/src/MEDS_tabular_automl/scripts/tabularize_static.py @@ -14,16 +14,16 @@ from omegaconf import DictConfig -from MEDS_tabular_automl.describe_codes import ( +from ..describe_codes import ( convert_to_df, filter_parquet, get_feature_columns, get_feature_freqs, ) -from MEDS_tabular_automl.file_name import list_subdir_files -from MEDS_tabular_automl.generate_static_features import get_flat_static_rep -from MEDS_tabular_automl.mapper import wrap as rwlock_wrap -from MEDS_tabular_automl.utils import ( +from ..file_name import list_subdir_files +from ..generate_static_features import get_flat_static_rep +from ..mapper import wrap as rwlock_wrap +from ..utils import ( STATIC_CODE_AGGREGATION, STATIC_VALUE_AGGREGATION, filter_to_codes, diff --git a/src/MEDS_tabular_automl/scripts/tabularize_time_series.py b/src/MEDS_tabular_automl/scripts/tabularize_time_series.py index c48e59a..c6ecc98 100644 --- a/src/MEDS_tabular_automl/scripts/tabularize_time_series.py +++ b/src/MEDS_tabular_automl/scripts/tabularize_time_series.py @@ -15,12 +15,12 @@ from loguru import logger from omegaconf import DictConfig -from MEDS_tabular_automl.describe_codes import filter_parquet, get_feature_columns -from MEDS_tabular_automl.file_name import list_subdir_files -from MEDS_tabular_automl.generate_summarized_reps import generate_summary -from MEDS_tabular_automl.generate_ts_features import get_flat_ts_rep -from MEDS_tabular_automl.mapper import wrap as rwlock_wrap -from MEDS_tabular_automl.utils import ( +from ..describe_codes import filter_parquet, get_feature_columns +from ..file_name import list_subdir_files +from ..generate_summarized_reps import generate_summary +from ..generate_ts_features import get_flat_ts_rep +from ..mapper import wrap as rwlock_wrap +from ..utils import ( STATIC_CODE_AGGREGATION, STATIC_VALUE_AGGREGATION, get_shard_prefix, diff --git a/src/MEDS_tabular_automl/utils.py b/src/MEDS_tabular_automl/utils.py index 9527aea..b4e9e81 100644 --- a/src/MEDS_tabular_automl/utils.py +++ b/src/MEDS_tabular_automl/utils.py @@ -78,7 +78,7 @@ def filter_to_codes( return sorted(feature_freqs["code"].to_list()) -OmegaConf.register_new_resolver("filter_to_codes", filter_to_codes) +OmegaConf.register_new_resolver("filter_to_codes", filter_to_codes, replace=True) def load_tqdm(use_tqdm: bool): From e39b934b73eceecd33ccccfb602395a4c7b52e68 Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 03:53:10 -0400 Subject: [PATCH 04/46] Fixed unexpected first-element-only issue with the readthedocs API. Format is still very off though. --- docs/source/conf.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1f90468..6cf386b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,4 +1,5 @@ import os +import shutil import sys # Configuration file for the Sphinx documentation builder. @@ -47,6 +48,24 @@ ".md": "markdown", } +# -- Run sphinx-apidoc +# This ensures we don't need to run apidoc manually. + +# TODO: use https://github.com/sphinx-extensions2/sphinx-autodoc2 + +from sphinx.ext import apidoc + +output_dir = __location__ / "generated" +module_dir = __src__ / "src/MEDS_tabular_automl" +if output_dir.is_dir(): + shutil.rmtree(output_dir) + +try: + cmd_line = f"--implicit-namespaces -e -f -o {output_dir} {module_dir}" + apidoc.main(cmd_line.split(" ")) +except Exception as e: # pylint: disable=broad-except + print(f"Running `sphinx-apidoc {cmd_line}` failed!\n{e}") + intersphinx_mapping = { "python": ("https://docs.python.org/3/", None), "sphinx": ("https://www.sphinx-doc.org/en/master/", None), From aeadfde7626e90e04e5cb635cec8d76bd7f06fd6 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 08:44:11 +0000 Subject: [PATCH 05/46] checkpointing rst --- docs/requirements.txt | 1 + docs/source/conf.py | 21 +++++++++++++++++++-- docs/source/index.rst | 2 ++ 3 files changed, 22 insertions(+), 2 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 85aba40..a6a9840 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -13,3 +13,4 @@ omegaconf ipywidgets ipykernel ipython +autoapi diff --git a/docs/source/conf.py b/docs/source/conf.py index 6cf386b..6d73a36 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -39,9 +39,26 @@ "sphinx.ext.napoleon", "sphinx_rtd_theme", "recommonmark", - # "sphinx_immaterial" + "sphinx.ext.viewcode", + "autoapi.extension", ] +# AutoAPI configuration +autoapi_type = "python" +autoapi_dirs = ["../../src"] # Adjust the path to your source code directory + +# Autodoc options +autodoc_default_options = { + "members": True, + "undoc-members": True, + "private-members": True, + "special-members": True, + "inherited-members": True, + "show-inheritance": True, +} + +# Autosummary options + source_suffix = { ".rst": "restructuredtext", ".txt": "markdown", @@ -98,7 +115,7 @@ # html_sidebars = {"**": ["logo-text.html", "globaltoc.html", "localtoc.html", "searchbox.html"]} html_theme_options = { - "dark_mode_code_blocks": False, + "dark_mode_code_blocks": True, # "nav_title": "MEDS-Tab", # "palette": {"primary": "green", "accent": "green"}, # "repo_url": "https://github.com/mmcdermott/MEDS_Tabular_AutoML", diff --git a/docs/source/index.rst b/docs/source/index.rst index 15737d3..07aa4d0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,3 +17,5 @@ Welcome to MEDS-Tab's documentation! computational-performance xgboost-performance api + + autoapi/index From 2f72bb8c316110cf146bbf7dd4cd5926d00bdf51 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 10:07:39 +0000 Subject: [PATCH 06/46] fixed docs --- .gitignore | 3 + docs/assets/main_fig.png | Bin 0 -> 411999 bytes docs/assets/meds_logo.png | Bin 0 -> 32672 bytes docs/assets/pivot.png | Bin 0 -> 686976 bytes docs/make.bat | 8 +- docs/requirements.txt | 6 +- docs/source/api.rst | 8 - docs/source/computational-performance.rst | 7 - docs/source/conf.py | 328 ++++++++++++++++++---- docs/source/implementation.md | 14 + docs/source/implementation.rst | 7 - docs/source/index.md | 48 ++++ docs/source/index.rst | 21 -- docs/source/installation.md | 34 +++ docs/source/installation.rst | 7 - docs/source/overview.md | 113 ++++++++ docs/source/overview.rst | 6 - docs/source/prediction.md | 5 + docs/source/profiling.md | 3 + docs/source/usage.rst | 7 - docs/source/xgboost-performance.rst | 6 - 21 files changed, 496 insertions(+), 135 deletions(-) create mode 100644 docs/assets/main_fig.png create mode 100644 docs/assets/meds_logo.png create mode 100644 docs/assets/pivot.png delete mode 100644 docs/source/api.rst delete mode 100644 docs/source/computational-performance.rst create mode 100644 docs/source/implementation.md delete mode 100644 docs/source/implementation.rst create mode 100644 docs/source/index.md delete mode 100644 docs/source/index.rst create mode 100644 docs/source/installation.md delete mode 100644 docs/source/installation.rst create mode 100644 docs/source/overview.md delete mode 100644 docs/source/overview.rst create mode 100644 docs/source/prediction.md create mode 100644 docs/source/profiling.md delete mode 100644 docs/source/usage.rst delete mode 100644 docs/source/xgboost-performance.rst diff --git a/.gitignore b/.gitignore index 57eed8c..95b15ab 100644 --- a/.gitignore +++ b/.gitignore @@ -70,6 +70,9 @@ instance/ # Sphinx documentation docs/_build/ +docs/source/_autosummary/ +docs/source/api/ +docs/source/bin/ # PyBuilder 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echo.http://sphinx-doc.org/ exit /b 1 ) -if "%1" == "" goto help - %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end @@ -32,4 +32,4 @@ goto end %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end -popd +popd diff --git a/docs/requirements.txt b/docs/requirements.txt index a6a9840..ad5a9f4 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,16 +1,14 @@ sphinx==7.1.2 sphinx-rtd-theme==1.3.0rc1 sphinx-collections -recommonmark -piccolo_theme +sphinx_immaterial sphinx_subfigure nbsphinx myst_parser pypandoc linkify-it-py -ipykernel omegaconf ipywidgets ipykernel ipython -autoapi +esgpt @ git+https://github.com/mmcdermott/EventStreamGPT diff --git a/docs/source/api.rst b/docs/source/api.rst deleted file mode 100644 index 463c193..0000000 --- a/docs/source/api.rst +++ /dev/null @@ -1,8 +0,0 @@ -API -==== - -.. autosummary:: - :toctree: generated - :recursive: - - src diff --git a/docs/source/computational-performance.rst b/docs/source/computational-performance.rst deleted file mode 100644 index 68a924c..0000000 --- a/docs/source/computational-performance.rst +++ /dev/null @@ -1,7 +0,0 @@ -Computational Performance vs. Existing Pipelines -================================================ - -.. include:: ../../README.md - :parser: markdown - :start-after: Computational Performance vs. Existing Pipelines - :end-before: XGBoost Model Performance on MIMIC-IV diff --git a/docs/source/conf.py b/docs/source/conf.py index 6d73a36..2b23957 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,6 +1,7 @@ import os import shutil import sys +from pathlib import Path # Configuration file for the Sphinx documentation builder. # @@ -16,11 +17,14 @@ release = "0.0.2" version = "0.0.2" -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html +language = "en" # -- Path setup -from pathlib import Path __location__ = Path(os.path.dirname(__file__)) __src__ = __location__ / "../.." @@ -30,40 +34,24 @@ # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, str(__src__)) -extensions = [ - "sphinx.ext.duration", - "sphinx.ext.doctest", - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinx.ext.intersphinx", - "sphinx.ext.napoleon", - "sphinx_rtd_theme", - "recommonmark", - "sphinx.ext.viewcode", - "autoapi.extension", -] -# AutoAPI configuration -autoapi_type = "python" -autoapi_dirs = ["../../src"] # Adjust the path to your source code directory - -# Autodoc options -autodoc_default_options = { - "members": True, - "undoc-members": True, - "private-members": True, - "special-members": True, - "inherited-members": True, - "show-inheritance": True, -} +def ensure_pandoc_installed(_): + """Source: https://stackoverflow.com/questions/62398231/building-docs-fails-due-to-missing-pandoc""" + import pypandoc -# Autosummary options + # Download pandoc if necessary. If pandoc is already installed and on + # the PATH, the installed version will be used. Otherwise, we will + # download a copy of pandoc into docs/bin/ and add that to our PATH. + pandoc_dir = str(__location__ / "bin") + # Add dir containing pandoc binary to the PATH environment variable + if pandoc_dir not in os.environ["PATH"].split(os.pathsep): + os.environ["PATH"] += os.pathsep + pandoc_dir + + pypandoc.ensure_pandoc_installed( + targetfolder=pandoc_dir, + delete_installer=True, + ) -source_suffix = { - ".rst": "restructuredtext", - ".txt": "markdown", - ".md": "markdown", -} # -- Run sphinx-apidoc # This ensures we don't need to run apidoc manually. @@ -72,7 +60,7 @@ from sphinx.ext import apidoc -output_dir = __location__ / "generated" +output_dir = __location__ / "api" module_dir = __src__ / "src/MEDS_tabular_automl" if output_dir.is_dir(): shutil.rmtree(output_dir) @@ -83,27 +71,146 @@ except Exception as e: # pylint: disable=broad-except print(f"Running `sphinx-apidoc {cmd_line}` failed!\n{e}") + +# -- General configuration + + +# -- Project information +extensions = [ + "sphinx.ext.duration", + "sphinx.ext.doctest", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.viewcode", + "sphinx.ext.coverage", + "sphinx.ext.ifconfig", + "sphinx.ext.mathjax", + "sphinx.ext.napoleon", + "sphinx.ext.imgconverter", + "sphinxcontrib.collections", + "sphinx_subfigure", + "myst_parser", + "nbsphinx", + # "sphinx_immaterial", +] +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +html_theme = "pydata_sphinx_theme" +html_sidebars = {"**": []} # ["logo-text.html", "globaltoc.html", "localtoc.html", "searchbox.html"] + + +nbsphinx_allow_errors = True + + +collections_dir = __location__ / "_collections" +if not collections_dir.is_dir(): + os.mkdir(collections_dir) + +python_version = ".".join(map(str, sys.version_info[0:2])) intersphinx_mapping = { - "python": ("https://docs.python.org/3/", None), - "sphinx": ("https://www.sphinx-doc.org/en/master/", None), + "sphinx": ("https://www.sphinx-doc.org/en/master", None), + "python": ("https://docs.python.org/" + python_version, None), + "matplotlib": ("https://matplotlib.org", None), + "numpy": ("https://numpy.org/doc/stable", None), + "sklearn": ("https://scikit-learn.org/stable", None), + "pandas": ("https://pandas.pydata.org/docs", None), + "pandera": ("https://pandera.readthedocs.io/en/stable", None), + "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), + "setuptools": ("https://setuptools.pypa.io/en/stable/", None), + "pyscaffold": ("https://pyscaffold.org/en/stable", None), + "hyperimpute": ("https://hyperimpute.readthedocs.io/en/latest/", None), + "xgbse": ("https://loft-br.github.io/xgboost-survival-embeddings/", None), + "lifelines": ("https://lifelines.readthedocs.io/en/stable/", None), + "optuna": ("https://optuna.readthedocs.io/en/stable/", None), } intersphinx_disabled_domains = ["std"] templates_path = ["_templates"] -exclude_patterns = [] -autosummary_generate = True +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +# Control options for included jupyter notebooks. +nb_execution_mode = "off" + + +# -- Options for HTML output + +# Configure MyST-Parser +myst_enable_extensions = [ + "amsmath", + "colon_fence", + "deflist", + "dollarmath", + "html_image", + "linkify", + "replacements", + "smartquotes", + "substitution", + "tasklist", +] + +myst_update_mathjax = True + +# MyST URL schemes. +myst_url_schemes = { + "http": None, + "https": None, + "ftp": None, + "mailto": None, + "repo-code": "https://github.com/mmcdermott/MEDS_Tabular_AutoML/tree/main/{{path}}#{{fragment}}", + # "doi": "https://doi.org/{{path}}", + # "gh-issue": { + # "url": "https://github.com/executablebooks/MyST-Parser/issue/{{path}}#{{fragment}}", + # "title": "Issue #{{path}}", + # "classes": ["github"], + # }, +} + +# The suffix of source filenames. +source_suffix = [".rst", ".md"] + +# The encoding of source files. +# source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = "index" +# The reST default role (used for this markup: `text`) to use for all documents. +default_role = "py:obj" + +# If true, '()' will be appended to :func: etc. cross-reference text. +# add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +# add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +# show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +# https://pygments.org/styles/ pygments_style = "tango" +# A list of ignored prefixes for module index sorting. +# modindex_common_prefix = [] -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output +# If true, keep warnings as "system message" paragraphs in the built documents. +# keep_warnings = False + +# If this is True, todo emits a warning for each TODO entries. The default is False. +todo_emit_warnings = True -# html_theme = "sphinx_rtd_theme" -html_theme = "piccolo_theme" -# html_theme = "sphinx_immaterial" -html_static_path = ["_static"] + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. html_title = f"MEDS-Tab v{version} Documentation" @@ -114,24 +221,129 @@ # html_sidebars = {"**": ["logo-text.html", "globaltoc.html", "localtoc.html", "searchbox.html"]} -html_theme_options = { - "dark_mode_code_blocks": True, - # "nav_title": "MEDS-Tab", - # "palette": {"primary": "green", "accent": "green"}, - # "repo_url": "https://github.com/mmcdermott/MEDS_Tabular_AutoML", - # "repo_name": "MEDS_Tabular_AutoML", - # # Visible levels of the global TOC; -1 means unlimited - # "globaltoc_depth": 3, - # If False, expand all TOC entries - "globaltoc_collapse": True, - # If True, show hidden TOC entries - "globaltoc_includehidden": False, -} +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. + +# Material theme options (see theme.conf for more information) +# html_theme_options = { +# # Set the name of the project to appear in the navigation. +# "nav_title": "MEDS-TAB", +# "palette": {"primary": "purple", "accent": "purple"}, +# # { +# # "media": "(prefers-color-scheme: light)", +# # "scheme": "default", +# # "toggle": { +# # "icon": "material/toggle-switch-off-outline", +# # "name": "Switch to dark mode", +# # }, +# # }, +# # { +# # "media": "(prefers-color-scheme: dark)", +# # "scheme": "slate", +# # "toggle": { +# # "icon": "material/toggle-switch", +# # "name": "Switch to light mode", +# # }, +# # }, +# # "color_primary": "green", +# # "color_accent": "green", +# # Set the repo location to get a badge with stats +# "repo_url": "https://github.com/mmcdermott/MEDS_Tabular_AutoML", +# "repo_name": "meds-tab", +# # Visible levels of the global TOC; -1 means unlimited +# "globaltoc_depth": 3, +# # If False, expand all TOC entries +# "globaltoc_collapse": True, +# # If True, show hidden TOC entries +# "globaltoc_includehidden": False, +# } + + +html_static_path = ["_static"] html_show_copyright = True htmlhelp_basename = "meds-tab-doc" +# -- Options for LaTeX output +# latex_engine = "xelatex" +latex_elements = { # type: ignore + # The paper size ("letterpaper" or "a4paper"). + "papersize": "letterpaper", + # The font size ("10pt", "11pt" or "12pt"). + "pointsize": "10pt", + # Additional stuff for the LaTeX preamble. + "preamble": "\n".join( + [ + r"\usepackage{svg}", + r"\DeclareUnicodeCharacter{2501}{-}", + r"\DeclareUnicodeCharacter{2503}{|}", + r"\DeclareUnicodeCharacter{2500}{-}", + r"\DeclareUnicodeCharacter{2550}{-}", + r"\DeclareUnicodeCharacter{2517}{+}", + r"\DeclareUnicodeCharacter{2518}{+}", + r"\DeclareUnicodeCharacter{2534}{+}", + r"\DeclareUnicodeCharacter{250C}{+}", + r"\DeclareUnicodeCharacter{252C}{+}", + r"\DeclareUnicodeCharacter{2510}{+}", + r"\DeclareUnicodeCharacter{2502}{|}", + r"\DeclareUnicodeCharacter{2506}{|}", + r"\DeclareUnicodeCharacter{2561}{|}", + r"\DeclareUnicodeCharacter{256A}{|}", + r"\DeclareUnicodeCharacter{2523}{|}", + r"\DeclareUnicodeCharacter{03BC}{\ensuremath{\mu}}", + r"\DeclareUnicodeCharacter{255E}{|}", + r"\DeclareUnicodeCharacter{255F}{+}", + r"\DeclareUnicodeCharacter{254E}{|}", + r"\DeclareUnicodeCharacter{257C}{-}", + r"\DeclareUnicodeCharacter{257E}{-}", + r"\DeclareUnicodeCharacter{2559}{+}", + ] + ), +} + + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, author, documentclass [howto/manual]). +latex_documents = [ + ( + "index", + "meds_tab_documentation.tex", + "MEDS-TAB Documentation", + r"Matthew McDermott \& Nassim Oufattole \& Teya Bergamaschi", + "manual", + ) +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +# latex_logo = "" + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +# latex_use_parts = False + +# If true, show page references after internal links. +# latex_show_pagerefs = False + +# If true, show URL addresses after external links. +# latex_show_urls = False + +# Documents to append as an appendix to all manuals. +# latex_appendices = [] + +# If false, no module index is generated. +# latex_domain_indices = True # -- Options for EPUB output epub_show_urls = "footnote" + +print(f"loading configurations for {project} {version} ...", file=sys.stderr) + + +def setup(app): + app.connect("builder-inited", ensure_pandoc_installed) diff --git a/docs/source/implementation.md b/docs/source/implementation.md new file mode 100644 index 0000000..4120b5c --- /dev/null +++ b/docs/source/implementation.md @@ -0,0 +1,14 @@ +# How does MEDS-Tab Work? + +#### What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? + +This is a common misconception. _Tabular_ data refers to data that can be organized in a consistent, logical +set of rows/columns such that the entirety of a "sample" or "instance" for modeling or analysis is contained +in a single row, and the set of columns possibly observed (there can be missingness) is consistent across all +rows. Structured EHR data does not satisfy this definition, as we will have different numbers of observations +of medical codes and values at different timestamps for different patients, so it cannot simultanesouly +satisfy the (1) "single row single instance", (2) "consistent set of columns", and (3) "logical" requirements. +Thus, in this pipeline, when we say we will produce a "tabular" view of MEDS data, we mean a dataset that can +realize these constraints, which will explicitly involve summarizing the patient data over various historical +or future windows in time to produce a single row per patient with a consistent, logical set of columns +(though there may still be missingness). diff --git a/docs/source/implementation.rst b/docs/source/implementation.rst deleted file mode 100644 index d03e1a6..0000000 --- a/docs/source/implementation.rst +++ /dev/null @@ -1,7 +0,0 @@ -How does MEDS-Tab Work? -======================= - -.. include:: ../../README.md - :parser: markdown - :start-after: How does MEDS-Tab Work? - :end-before: Computational Performance vs. Existing Pipelines diff --git a/docs/source/index.md b/docs/source/index.md new file mode 100644 index 0000000..723af56 --- /dev/null +++ b/docs/source/index.md @@ -0,0 +1,48 @@ +# Welcome! + +ACES is a library designed for the automatic extraction of cohorts from event-stream datasets for downstream machine learning tasks. Check out below for an overview of ACES and how it could be useful in your workflows! + +```{toctree} +--- +glob: +maxdepth: 2 +--- +Overview +Pipeline/Architecture Details +Computational Profile +Prediction Performance +Module API Reference +``` + +______________________________________________________________________ + +## Why ACES? + +If you have a dataset and want to leverage it for machine learning tasks, the ACES ecosystem offers a streamlined and user-friendly approach. Here's how you can easily transform, prepare, and utilize your dataset with MEDS and ACES for efficient and effective machine learning: + +### I. Transform to MEDS + +- Simplicity: Converting your dataset to the Medical Event Data Standard (MEDS) is straightforward and user-friendly compared to other Common Data Models (CDMs). +- Minimal Bias: This conversion process ensures that your data remains as close to its raw form as possible, minimizing the introduction of biases. +- [MEDS-ETL](https://github.com/Medical-Event-Data-Standard/meds_etl): Follow this link for detailed instructions and ETLs to transform your dataset into the MEDS format! + +### II. Identify Predicates + +- Task-Specific Concepts: Identify the predicates (data concepts) required for your specific machine learning tasks. +- Pre-Defined Criteria: Utilize our pre-defined criteria across various tasks and clinical areas to expedite this process. +- [PIE-MD](https://github.com/mmcdermott/PIE_MD/tree/main/tasks/criteria): Access our repository of tasks to find relevant predicates! + +### III. Set Dataset-Agnostic Criteria + +- Standardization: Combine the identified predicates with standardized, dataset-agnostic criteria files. +- Examples: Refer to the [MIMIC-IV](https://github.com/mmcdermott/PIE_MD/tree/main/tasks/MIMIC-IV) and [eICU](https://github.com/mmcdermott/PIE_MD/tree/main/tasks/eICU) examples for guidance on how to structure your criteria files for your private datasets! + +### IV. Run ACES + +- Run the ACES Command-Line Interface tool (`aces-cli`) to extract cohorts based on your task - check out the [Usage Guide](https://eventstreamaces.readthedocs.io/en/latest/usage.html)! + +### V. Run MEDS-Tab + +- Painless Reproducibility: Use [MEDS-Tab](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV/tree/main/tasks) to obtain comparable, reproducible, and well-tuned XGBoost results tailored to your dataset-specific feature space! + +By following these steps, you can seamlessly transform your dataset, define necessary criteria, and leverage powerful machine learning tools within the ACES ecosystem. This approach not only simplifies the process but also ensures high-quality, reproducible results for your machine learning for health projects. It can reliably take no more than a week of full-time human effort to perform Steps I-V on new datasets in reasonable raw formulations! diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 07aa4d0..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. MEDS-TAB documentation master file, created by - sphinx-quickstart on Mon Jun 3 20:41:52 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to MEDS-Tab's documentation! -==================================== - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - overview - installation - usage - implementation - computational-performance - xgboost-performance - api - - autoapi/index diff --git a/docs/source/installation.md b/docs/source/installation.md new file mode 100644 index 0000000..1bceaab --- /dev/null +++ b/docs/source/installation.md @@ -0,0 +1,34 @@ +# Scalable tabularization and tabular feature usage utilities over generic MEDS datasets + +

+ Python + PyPI + Hydra + Codecov + Tests + Code Quality + Documentation Status + Contributors + Pull Requests + License +

+ +This repository provides utilities and scripts to run limited automatic tabular ML pipelines for generic MEDS +datasets. + +# Installation + +To use MEDS-Tab, install the dependencies following commands below: + +**Pip Install** + +```bash +pip install meds-tab +``` + +**Local Install** + +``` +# clone the git repo +pip install . +``` diff --git a/docs/source/installation.rst b/docs/source/installation.rst deleted file mode 100644 index fe44a54..0000000 --- a/docs/source/installation.rst +++ /dev/null @@ -1,7 +0,0 @@ -Installation -============ - -.. include:: ../../README.md - :parser: markdown - :start-after: Installation - :end-before: Usage diff --git a/docs/source/overview.md b/docs/source/overview.md new file mode 100644 index 0000000..ca59278 --- /dev/null +++ b/docs/source/overview.md @@ -0,0 +1,113 @@ +# Overview + +This repository consists of two key pieces: + +1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing + patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) either backwards or + forwards in time from a given index date. Naturally, only "look-back" windows should be used for + future-event prediction tasks, and are thus currently implemented. +2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression + downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- + what is more advanced is the efficient construction, storage, and loading of tabular features for the + candidate AutoML models, enabling a far more extensive search over different featurization strategies. + +### Scripts and Examples + +See `tests/test_integration.py` for an example of the end-to-end pipeline being run on synthetic data. This +script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. + +For an end to end example over MIMIC-IV, see the [companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV) +For an end to end example over Philips eICU, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). + +### Core CLI Scripts Overview + +1. **`meds-tab-describe`**: This command processes MEDS data shards to compute the frequencies of different code-types + + - time-series codes (codes with timestamps) + - time-series numerical values (codes with timestamps and numerical values) + - static codes (codes without timestamps) + - static numerical codes (codes without timestamps but with numerical values). + + **Caching feature names and frequencies** in a dataset stored in `"path_to_data"` + + ``` + meds-tab-describe MEDS_cohort_dir="path_to_data" + ``` + +2. **`meds-tab-tabularize-static`**: Filters and processes the dataset based on the frequency of codes, generating a tabular vector for each patient at each timestamp in the shards. Each row corresponds to a unique `patient_id` and `timestamp` combination, thus rows are duplicated across multiple timestamps for the same patient. + + **Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` + + ``` + meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" + ``` + +3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's `--multirun` flag and the `joblib` launcher. + + **Aggregates time-series data** on features across different `window_sizes` + + ``` + meds-tab-tabularize-time-series --multirun \ + worker="range(0,$N_PARALLEL_WORKERS)" \ + hydra/launcher=joblib \ + MEDS_cohort_dir="path_to_data" \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + ``` + +4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. + + **Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) + + ``` + meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + ``` + +5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). + + ``` + meds-tab-xgboost --multirun \ + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + ``` + +6. **`meds-tab-xgboost-sweep`**: Conducts an Optuna hyperparameter sweep to optimize over `window_sizes`, `aggregations`, and `min_code_inclusion_frequency`, aiming to enhance model performance and adaptability. + +### Additional CLI Scripts + +1. **`generate-permutations`**: Generates and prints a sorted list of all permutations from a comma separated input. This is provided for the convenience of sweeping over all possible combinations of window sizes and aggregations. + + For example you can directly call **`generate-permutations`** in the command line: + + ```bash + generate-permutations [2,3,4] + [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] + ``` + + This could be used in the command line in concert with other calls. For example, the following call: + + ```bash + meds-tab-xgboost --multirun tabularization.window_sizes=$(generate-permutations [1d,2d,7d,full]) + ``` + + would resolve to: + + ```bash + meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] + ``` diff --git a/docs/source/overview.rst b/docs/source/overview.rst deleted file mode 100644 index c19436c..0000000 --- a/docs/source/overview.rst +++ /dev/null @@ -1,6 +0,0 @@ -Overview -======== - -.. include:: ../../README.md - :parser: markdown - :end-before: Installation diff --git a/docs/source/prediction.md b/docs/source/prediction.md new file mode 100644 index 0000000..3c9d5c7 --- /dev/null +++ b/docs/source/prediction.md @@ -0,0 +1,5 @@ +# XGBoost Performance + +## XGBoost Model Performance on MIMIC-IV + +## XGBoost Model Performance on Philips eICU diff --git a/docs/source/profiling.md b/docs/source/profiling.md new file mode 100644 index 0000000..cf8f9c1 --- /dev/null +++ b/docs/source/profiling.md @@ -0,0 +1,3 @@ +# Computational Performance vs. Existing Pipelines + +TODO diff --git a/docs/source/usage.rst b/docs/source/usage.rst deleted file mode 100644 index 684bc43..0000000 --- a/docs/source/usage.rst +++ /dev/null @@ -1,7 +0,0 @@ -Usage -====== - -.. include:: ../../README.md - :parser: markdown - :start-after: Usage - :end-before: How does MEDS-Tab Work? diff --git a/docs/source/xgboost-performance.rst b/docs/source/xgboost-performance.rst deleted file mode 100644 index 63e41b1..0000000 --- a/docs/source/xgboost-performance.rst +++ /dev/null @@ -1,6 +0,0 @@ -XGBoost Performance on MIMIC-IV and Philips eICU -================================================ - -.. include:: ../../README.md - :parser: markdown - :start-after: XGBoost Performance From b0365a64366d1903441466e7a1da911d65dbeba2 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 10:31:02 +0000 Subject: [PATCH 07/46] updated requirements --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index ad5a9f4..7f214a7 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -11,4 +11,4 @@ omegaconf ipywidgets ipykernel ipython -esgpt @ git+https://github.com/mmcdermott/EventStreamGPT +pydata-sphinx-theme From b2655a6e36839feebd518a5ef5ddcd2a819216da Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 06:43:59 -0400 Subject: [PATCH 08/46] Add PDF format --- .readthedocs.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index fff2f4b..7b84363 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -11,3 +11,6 @@ python: sphinx: configuration: docs/source/conf.py +# Optionally build your docs in additional formats such as PDF +formats: + - pdf From 0bcde72fd0d6da112b7c2089c84ed635e5a53621 Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 06:58:34 -0400 Subject: [PATCH 09/46] trying a different readthedocs config for dependency management --- .readthedocs.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 7b84363..0924093 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -8,6 +8,10 @@ build: python: install: - requirements: docs/requirements.txt + - method: pip + path: . + extra_requirements: + - docs sphinx: configuration: docs/source/conf.py From 1fe9186a949d04b09001b0ddf53db2bd686cf699 Mon Sep 17 00:00:00 2001 From: Hyewon Jeong Date: Thu, 13 Jun 2024 04:32:20 -0700 Subject: [PATCH 10/46] Update index.md --- docs/source/index.md | 32 +++++++++----------------------- 1 file changed, 9 insertions(+), 23 deletions(-) diff --git a/docs/source/index.md b/docs/source/index.md index 723af56..8548a17 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -1,6 +1,6 @@ # Welcome! -ACES is a library designed for the automatic extraction of cohorts from event-stream datasets for downstream machine learning tasks. Check out below for an overview of ACES and how it could be useful in your workflows! +MEDS-Tab is a library designed for automated tabularization, data preparation with aggregation, and windowing. Check out below for an overview of MEDS-Tab and how it could be useful in your workflows! ```{toctree} --- @@ -16,33 +16,19 @@ Module API Reference ______________________________________________________________________ -## Why ACES? +## Why MEDS-Tab? -If you have a dataset and want to leverage it for machine learning tasks, the ACES ecosystem offers a streamlined and user-friendly approach. Here's how you can easily transform, prepare, and utilize your dataset with MEDS and ACES for efficient and effective machine learning: +MEDS-Tab is a comprehensive framework designed to streamline the handling, modeling, and analysis of complex medical time-series data. By leveraging automated processes, MEDS-Tab significantly reduces the computation required to generate high-quality baseline models for diverse supervised learning tasks. ### I. Transform to MEDS -- Simplicity: Converting your dataset to the Medical Event Data Standard (MEDS) is straightforward and user-friendly compared to other Common Data Models (CDMs). -- Minimal Bias: This conversion process ensures that your data remains as close to its raw form as possible, minimizing the introduction of biases. -- [MEDS-ETL](https://github.com/Medical-Event-Data-Standard/meds_etl): Follow this link for detailed instructions and ETLs to transform your dataset into the MEDS format! +- Cost Efficiency: MEDS-Tab is dramatically more cost-effective compared to existing solutions +- Strong Performance: MEDS-Tab produces robust and high-performance across various datasets compared with other frameworks. -### II. Identify Predicates - -- Task-Specific Concepts: Identify the predicates (data concepts) required for your specific machine learning tasks. -- Pre-Defined Criteria: Utilize our pre-defined criteria across various tasks and clinical areas to expedite this process. -- [PIE-MD](https://github.com/mmcdermott/PIE_MD/tree/main/tasks/criteria): Access our repository of tasks to find relevant predicates! - -### III. Set Dataset-Agnostic Criteria - -- Standardization: Combine the identified predicates with standardized, dataset-agnostic criteria files. -- Examples: Refer to the [MIMIC-IV](https://github.com/mmcdermott/PIE_MD/tree/main/tasks/MIMIC-IV) and [eICU](https://github.com/mmcdermott/PIE_MD/tree/main/tasks/eICU) examples for guidance on how to structure your criteria files for your private datasets! - -### IV. Run ACES - -- Run the ACES Command-Line Interface tool (`aces-cli`) to extract cohorts based on your task - check out the [Usage Guide](https://eventstreamaces.readthedocs.io/en/latest/usage.html)! - -### V. Run MEDS-Tab +### II. Run MEDS-Tab +- Run the MEDS-Tab Command-Line Interface tool (`MEDS-Tab-cli`) to extract cohorts based on your task - check out the [Usage Guide](https://meds-tab--36.org.readthedocs.build/en/36/overview.html#core-cli-scripts-overview)! + - Painless Reproducibility: Use [MEDS-Tab](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV/tree/main/tasks) to obtain comparable, reproducible, and well-tuned XGBoost results tailored to your dataset-specific feature space! -By following these steps, you can seamlessly transform your dataset, define necessary criteria, and leverage powerful machine learning tools within the ACES ecosystem. This approach not only simplifies the process but also ensures high-quality, reproducible results for your machine learning for health projects. It can reliably take no more than a week of full-time human effort to perform Steps I-V on new datasets in reasonable raw formulations! +By following these steps, you can seamlessly transform your dataset, define necessary criteria, and leverage powerful machine learning tools within the MEDS-Tab ecosystem. This approach not only simplifies the process but also ensures high-quality, reproducible results for your machine learning for health projects. It can reliably take no more than a week of full-time human effort to perform Steps I-V on new datasets in reasonable raw formulations! From 14bad2b05be04acd64f449e393e0237d267bf4dc Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 07:41:26 -0400 Subject: [PATCH 11/46] Added appendix A to README --- README.md | 91 ++++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 63 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 1ce978f..4066c53 100644 --- a/README.md +++ b/README.md @@ -37,43 +37,49 @@ pip install . This repository consists of two key pieces: -1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing - patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) either backwards or - forwards in time from a given index date. Naturally, only "look-back" windows should be used for - future-event prediction tasks, and are thus currently implemented. -2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression - downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- - what is more advanced is the efficient construction, storage, and loading of tabular features for the - candidate AutoML models, enabling a far more extensive search over different featurization strategies. +1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) backwards in time from a given index date. +2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- what is more advanced is the efficient construction, storage, and loading of tabular features for the candidate AutoML models, enabling a far more extensive search over a much larger total number of features than prior systems. -## Scripts and Examples +## Quick Start + +To use MEDS-Tab, install the dependencies following commands below: + +**Pip Install** + +```bash +pip install meds-tab +``` + +**Local Install** + +```bash +# clone the git repo +pip install . +``` -See `tests/test_integration.py` for an example of the end-to-end pipeline being run on synthetic data. This -script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. +## Scripts and Examples -For an end to end example over MIMIC-IV, see the [companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV) +For an end to end example over MIMIC-IV, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). For an end to end example over Philips eICU, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). +See `tests/test_integration.py` for a local example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. + ## Core CLI Scripts Overview -1. **`meds-tab-describe`**: This command processes MEDS data shards to compute the frequencies of different code-types +1. **`meds-tab-describe`**: This command processes MEDS data shards to compute the frequencies of different code-types. It differentiates codes into the following categories: - time-series codes (codes with timestamps) - time-series numerical values (codes with timestamps and numerical values) - static codes (codes without timestamps) - static numerical codes (codes without timestamps but with numerical values). - **Caching feature names and frequencies** in a dataset stored in `"path_to_data"` - - ``` - meds-tab-describe MEDS_cohort_dir="path_to_data" - ``` + This script further caches feature names and frequencies in a dataset stored in a `code_metadata.parquet` file within the `MEDS_cohort_dir` argument specified as a hydra-style command line argument. 2. **`meds-tab-tabularize-static`**: Filters and processes the dataset based on the frequency of codes, generating a tabular vector for each patient at each timestamp in the shards. Each row corresponds to a unique `patient_id` and `timestamp` combination, thus rows are duplicated across multiple timestamps for the same patient. - **Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` + **Example: Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` - ``` + ```bash meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=[1d,30d,365d,full] \ @@ -81,11 +87,13 @@ For an end to end example over Philips eICU, see the [eICU companion repository] tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" ``` -3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's `--multirun` flag and the `joblib` launcher. + - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) - **Aggregates time-series data** on features across different `window_sizes` +3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's [`--multirun`](https://hydra.cc/docs/intro/#multirun) flag and the [`joblib` launcher](https://hydra.cc/docs/plugins/joblib_launcher/#internaldocs-banner). - ``` + **Example: Aggregate time-series data** on features across different `window_sizes` + + ```bash meds-tab-tabularize-time-series --multirun \ worker="range(0,$N_PARALLEL_WORKERS)" \ hydra/launcher=joblib \ @@ -98,9 +106,9 @@ For an end to end example over Philips eICU, see the [eICU companion repository] 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. - **Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) + **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) - ``` + ```bash meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ tabularization.min_code_inclusion_frequency=10 \ @@ -111,7 +119,7 @@ For an end to end example over Philips eICU, see the [eICU companion repository] 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). - ``` + ```bash meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -122,8 +130,6 @@ For an end to end example over Philips eICU, see the [eICU companion repository] tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` -6. **`meds-tab-xgboost-sweep`**: Conducts an Optuna hyperparameter sweep to optimize over `window_sizes`, `aggregations`, and `min_code_inclusion_frequency`, aiming to enhance model performance and adaptability. - ## Additional CLI Scripts 1. **`generate-permutations`**: Generates and prints a sorted list of all permutations from a comma separated input. This is provided for the convenience of sweeping over all possible combinations of window sizes and aggregations. @@ -147,6 +153,35 @@ For an end to end example over Philips eICU, see the [eICU companion repository] meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] ``` + which can then be correctly interpreted by Hydra's multirun logic. + +## Roadmap + +MEDS-Tab has several key limitations which we plan to address in future changes. These include, and are tracked by, the following GitHub issues. + +### Improvements to the core tabularization + +1. Further memory and runtime improvements are possible: [#16](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/16) +2. We should support additional window sizes and types: [#31](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/31) +3. We should support additional aggregation functions: [#32](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/32) + +### Improvements to the modeling pipeline + +1. We should likely decorrelate the default aggregations and/or window sizes we use prior to passing them into the models as features: [#27](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/27) +2. We need to do a detailed parameter study over the hyperparameter sweep options to find good defaults for these kinds of problems and models: [#33](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/33) +3. We should support a more extensive set of pipeline operations and model architectures: [#37](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/37) + +### Technical debt / code improvements + +1. The computation and use of the code metadata dataframe, containing frequencies of codes, should be offloaded to core MEDS functionality, with the remaining code in this repository cleaned up. + - [#28](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/28) + - [#14](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/14) +2. We should add more doctests and push test coverage up to 100% + - [#29](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/29) + - [#30](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/30) +3. We need to ensure full and seamless compatibility with the ACES CLI tool, rather than relying on the python API and manual adjustments: + [#34](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/34) + # How does MEDS-Tab Work? ## What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? From 5f75c2f56731ddca7b20a11c3ad4e7587e01cf4d Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 07:43:36 -0400 Subject: [PATCH 12/46] Added B --- README.md | 109 +++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 108 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4066c53..e893b1e 100644 --- a/README.md +++ b/README.md @@ -197,7 +197,114 @@ realize these constraints, which will explicitly involve summarizing the patient or future windows in time to produce a single row per patient with a consistent, logical set of columns (though there may still be missingness). -## Implementation Improvements +## The MEDS-Tab Architecture + +In this section we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. + +We break our method into 4 discrete parts + +1. Describe codes (compute feature frequencies) +2. Given time series data tabularize it +3. cache task specific rows of data for efficient loading +4. XGBoost training + +### 1. Describe Codes (Compute Feature Frequencies) + +This initial stage processes a pre-shareded dataset. We expect a structure as follows where each shard contains a subset of the patients: + +``` +/PATH/TO/MEDS/DATA +│ +└─── +│ │ .parquet +│ │ .parquet +│ │ ... +│ +└─── +│ │ .parquet +│ │ .parquet +| │ ... +| +... +``` + +We then compute and store feature frequencies, crucial for determining which features are relevant for further analysis. + +**Detailed Workflow:** + +- **Data Loading and Sharding**: We iterate through shards to compute feature frequencies for each shard. +- **Frequency Aggregation**: After computing frequencies across shards, we aggregate them to get a final count of each feature across the entire dataset training dataset, which allows us to filter out infrequent features in the tabularization stage or when tuning XGBoost. + +This outputs parquet file \`\` + +### 2. Tabularization of Time Series Data + +### Overview + +The tabularization stage of our pipeline, exposed via the cli commands: + +- `meds-tab-tabularize-static` for tabularizing static data +- and `meds-tab-tabularize-time-series` for tabularizing the time series data + +Static data is relatively small in the medical datasets, so we use a dense pivot operation, convert it to a sparse matrix, and then duplicate rows such that the static data will match up with the time series data rows generated in the next step. Static data is currently processed serially. + +The script for tabularizing time series data primarily transforms a raw, unstructured dataset into a structured, feature-rich dataset by utilizing a series of sophisticated data processing steps. This transformation involves converting raw time series from a Polars dataframe into a sparse matrix format, aggregating events that occur at the same date for the same patient, and then applying rolling window aggregations to extract temporal features. Here's a step-by-step breakdown of the algorithm: + +### High-Level Steps + +1. **Data Loading and Categorization**: + + - The script iterates through shards of patients, and shards can be processed in parallel using hydras joblib to launch multiple processes. + +2. **Sparse Matrix Conversion**: + + - Data from the Polars dataframe is converted into a sparse matrix format. This step is crucial for efficient memory management, especially when dealing with large datasets. + +3. **Event Aggregation**: + + - Events that occur on the same date for the same patient are aggregated. This reduces redundancy in the data and significantly speeds up the rolling window aggregations on datasets that have lots of concurrent observations. + +4. **Rolling Window Aggregation**: + + - The aggregated data undergoes a rolling window operation where various statistical methods are applied (sum, count, min, max, etc.) to extract features over specified window sizes. + +5. **Output Storage**: + + - Sparse array is converted to Coordinate List (COO) format and stored as a `.npz` file on disk. + - The file paths look as follows + +``` +/PATH/TO/MEDS/TABULAR_DATA +│ +└─── + ├─── + │ ├───code + │ │ └───count.npz + │ └───value + │ └───sum.npz + ... +``` + +### 3. Efficient Data Caching for Task-Specific Rows + +Now that we have generated tabular features for all the events in our dataset, we can cache subsets relevant for each task we wish to train a supervised model on. This step is critical for efficiently training machine learning models on task-specific data without having to load the entire dataset. + +**Detailed Workflow:** + +- **Row Selection Based on Tasks**: Only the data rows that are relevant to the specific tasks are selected and cached. This reduces the memory footprint and speeds up the training process. +- **Use of Sparse Matrices for Efficient Storage**: Sparse matrices are again employed here to store the selected data efficiently, ensuring that only non-zero data points are kept in memory, thus optimizing both storage and retrieval times. + +The file structure for the cached data mirrors the tabular data and alsi is `.npz` files, and users must specify the directory to labels that follow the same shard filestructure as the input meds data from step (1). Label parquets need `patient_id`, `timestamp`, and `label` columns. + +### 4. XGBoost Training + +The final stage uses the processed and cached data to train an XGBoost model. This stage is optimized to handle the sparse data structures produced in earlier stages efficiently. + +**Detailed Workflow:** + +- **Iterator for Data Loading**: Custom iterators are designed to load sparse matrices efficiently into the XGBoost training process, which can handle sparse inputs natively, thus maintaining high computational efficiency. +- **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance. +- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimu code inclusion frequency. # Computational Performance vs. Existing Pipelines From 0daab405c3940fad793a545ca1acd99e09685c62 Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 07:44:30 -0400 Subject: [PATCH 13/46] Added C --- README.md | 92 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) diff --git a/README.md b/README.md index e893b1e..04c4b44 100644 --- a/README.md +++ b/README.md @@ -307,6 +307,98 @@ The final stage uses the processed and cached data to train an XGBoost model. Th - **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimu code inclusion frequency. # Computational Performance vs. Existing Pipelines +Evaluating the computational overhead of tabularization methods is essential for assessing their efficiency and suitability for large-scale medical data processing. This section presents a comparative analysis of the computational overhead of MEDS-Tab with other systems like Catabra and TSFresh. It outlines the performance of each system in terms of wall time, memory usage, and output size, highlighting the computational efficiency and scalability of MEDS-Tab. + +--- + +## 1. System Comparison Overview + +The systems compared in this study represent different approaches to data tabularization, with the main difference being MEDS-Tab usage of sparse tabularization. Specifically, for comparison we used: +1. **Catabra/Catabra-Mem**: Offers data processing capabilities for time-series medical data, with variations to test memory management. +2. **TSFresh**: Both known and used for extensive feature extraction capabilities. + +The benchmarking tests were conducted using the following hardware and software settings: +- **CPU Specification**: 2 x AMD EPYC 7713 64-Core Processor +- **RAM Specification: 1024GB, 3200MHz, DDR4 +- **Software Environment**: Ubuntu 22.04.4 LTS + +## MEDS-Tab Tabularization Technique + +Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMICIV and EICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for EICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. + + + +## 2. Comparative Performance Analysis + +The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omited from the tables as it never completed within the 10 minute budget. + + +## eICU Dataset +--- + +The only method that was able to tabularize eICU data was MEDS-Tab. We ran our method with both 100 and 500 patients, resulting in an increment by three times in the number of codes. MEDS-Tab gave efficient results in terms of both time and memory usage. + +a) 100 Patients + +**Table 1: 6,374 Codes, 2,065,608 Rows, Output Shape [132,461, 6,374]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +|-----------|-------------|--------------|-------------|----------| +| 0m39.426s | 5,271.85 MB| 14,791.83 MB| 362 MB | meds_tab | + + +b) 500 Patients + + +**Table 2: 18,314 Codes, 8,737,355 Rows, Output Shape [565,014, 18,314]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +|-----------|-------------|--------------|-------------|----------| +| 3m4.435s | 8,335.44 MB| 15,102.55 MB| 1,326 MB | meds_tab | + + +## MIMIC-IV Dataset +--- + +MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC_IV. + +a) 10 Patients + +This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming TSFresh and Catabra in terms of both time and memory efficiency. + +**Table 3: 1,504 Codes, 23,346 Rows, Output Shape [2,127, 1,504]** +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +|------------|--------------|---------------|-------------|----------| +| 0m2.071s | 423.78 MB | 943.984 MB | 7 MB | meds_tab | +| 1m41.920s | 84,159.44 MB| 265,877.86 MB| 1 MB | tsfresh | +| 0m15.366s | 2,537.46 MB | 4,781.824 MB | 1 MB | catabra | + + +b) 100 Patients + +For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to TSFresh and Catabra. The performance gap was further highlighted with an increased number of patients and codes. + +**Table 4: 4,154 Codes, 150,789 Rows, Output Shape [15,664, 4,154]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +|------------|-----------------|---------------|-------------|----------| +| 0m4.724s | 718.76 MB | 1,167.29 MB | 45 MB | meds_tab | +| 5m9.077s | 217,477.52 MB | 659,735.25 MB| 4 MB | tsfresh | +| 3m17.671s | 14,319.53 MB | 28,342.81 MB | 4 MB | catabra | + + + +c) 500 Patients + +Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of ???, we could not get results for Catabra and TSFresh. + +**Table 5: 48,115 Codes, 795,368 Rows, Output Shape [75,595, 8,115]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +|-----------|--------------|-------------|--------------|----------| +| 0m15.867s | 1,410.79 MB | 3,539.32 MB| 442 MB | meds_tab | + +--- # XGBoost Performance From 7929e5a16dd2cdda7717356ab78fcbf0b995d520 Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 07:45:28 -0400 Subject: [PATCH 14/46] Added D --- README.md | 231 ++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 191 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index 04c4b44..052a66c 100644 --- a/README.md +++ b/README.md @@ -307,101 +307,252 @@ The final stage uses the processed and cached data to train an XGBoost model. Th - **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimu code inclusion frequency. # Computational Performance vs. Existing Pipelines + Evaluating the computational overhead of tabularization methods is essential for assessing their efficiency and suitability for large-scale medical data processing. This section presents a comparative analysis of the computational overhead of MEDS-Tab with other systems like Catabra and TSFresh. It outlines the performance of each system in terms of wall time, memory usage, and output size, highlighting the computational efficiency and scalability of MEDS-Tab. ---- +______________________________________________________________________ ## 1. System Comparison Overview The systems compared in this study represent different approaches to data tabularization, with the main difference being MEDS-Tab usage of sparse tabularization. Specifically, for comparison we used: + 1. **Catabra/Catabra-Mem**: Offers data processing capabilities for time-series medical data, with variations to test memory management. 2. **TSFresh**: Both known and used for extensive feature extraction capabilities. The benchmarking tests were conducted using the following hardware and software settings: + - **CPU Specification**: 2 x AMD EPYC 7713 64-Core Processor -- **RAM Specification: 1024GB, 3200MHz, DDR4 +- \*\*RAM Specification: 1024GB, 3200MHz, DDR4 - **Software Environment**: Ubuntu 22.04.4 LTS ## MEDS-Tab Tabularization Technique Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMICIV and EICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for EICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. - - ## 2. Comparative Performance Analysis The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omited from the tables as it never completed within the 10 minute budget. +## eICU Dataset -## eICU Dataset ---- +______________________________________________________________________ The only method that was able to tabularize eICU data was MEDS-Tab. We ran our method with both 100 and 500 patients, resulting in an increment by three times in the number of codes. MEDS-Tab gave efficient results in terms of both time and memory usage. a) 100 Patients -**Table 1: 6,374 Codes, 2,065,608 Rows, Output Shape [132,461, 6,374]** +**Table 1: 6,374 Codes, 2,065,608 Rows, Output Shape \[132,461, 6,374\]** | Wall Time | Avg Memory | Peak Memory | Output Size | Method | -|-----------|-------------|--------------|-------------|----------| -| 0m39.426s | 5,271.85 MB| 14,791.83 MB| 362 MB | meds_tab | - +| --------- | ----------- | ------------ | ----------- | -------- | +| 0m39.426s | 5,271.85 MB | 14,791.83 MB | 362 MB | meds_tab | b) 500 Patients - -**Table 2: 18,314 Codes, 8,737,355 Rows, Output Shape [565,014, 18,314]** +**Table 2: 18,314 Codes, 8,737,355 Rows, Output Shape \[565,014, 18,314\]** | Wall Time | Avg Memory | Peak Memory | Output Size | Method | -|-----------|-------------|--------------|-------------|----------| -| 3m4.435s | 8,335.44 MB| 15,102.55 MB| 1,326 MB | meds_tab | - +| --------- | ----------- | ------------ | ----------- | -------- | +| 3m4.435s | 8,335.44 MB | 15,102.55 MB | 1,326 MB | meds_tab | ## MIMIC-IV Dataset ---- + +______________________________________________________________________ MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC_IV. -a) 10 Patients +a) 10 Patients This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming TSFresh and Catabra in terms of both time and memory efficiency. -**Table 3: 1,504 Codes, 23,346 Rows, Output Shape [2,127, 1,504]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -|------------|--------------|---------------|-------------|----------| -| 0m2.071s | 423.78 MB | 943.984 MB | 7 MB | meds_tab | -| 1m41.920s | 84,159.44 MB| 265,877.86 MB| 1 MB | tsfresh | -| 0m15.366s | 2,537.46 MB | 4,781.824 MB | 1 MB | catabra | +**Table 3: 1,504 Codes, 23,346 Rows, Output Shape \[2,127, 1,504\]** +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ------------ | ------------- | ----------- | -------- | +| 0m2.071s | 423.78 MB | 943.984 MB | 7 MB | meds_tab | +| 1m41.920s | 84,159.44 MB | 265,877.86 MB | 1 MB | tsfresh | +| 0m15.366s | 2,537.46 MB | 4,781.824 MB | 1 MB | catabra | -b) 100 Patients +b) 100 Patients For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to TSFresh and Catabra. The performance gap was further highlighted with an increased number of patients and codes. -**Table 4: 4,154 Codes, 150,789 Rows, Output Shape [15,664, 4,154]** - -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -|------------|-----------------|---------------|-------------|----------| -| 0m4.724s | 718.76 MB | 1,167.29 MB | 45 MB | meds_tab | -| 5m9.077s | 217,477.52 MB | 659,735.25 MB| 4 MB | tsfresh | -| 3m17.671s | 14,319.53 MB | 28,342.81 MB | 4 MB | catabra | +**Table 4: 4,154 Codes, 150,789 Rows, Output Shape \[15,664, 4,154\]** +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ------------- | ------------- | ----------- | -------- | +| 0m4.724s | 718.76 MB | 1,167.29 MB | 45 MB | meds_tab | +| 5m9.077s | 217,477.52 MB | 659,735.25 MB | 4 MB | tsfresh | +| 3m17.671s | 14,319.53 MB | 28,342.81 MB | 4 MB | catabra | - -c) 500 Patients +c) 500 Patients Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of ???, we could not get results for Catabra and TSFresh. -**Table 5: 48,115 Codes, 795,368 Rows, Output Shape [75,595, 8,115]** +**Table 5: 48,115 Codes, 795,368 Rows, Output Shape \[75,595, 8,115\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -|-----------|--------------|-------------|--------------|----------| -| 0m15.867s | 1,410.79 MB | 3,539.32 MB| 442 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ----------- | ----------- | ----------- | -------- | +| 0m15.867s | 1,410.79 MB | 3,539.32 MB | 442 MB | meds_tab | ---- +______________________________________________________________________ # XGBoost Performance -## XGBoost Model Performance on MIMIC-IV +## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks + +Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. + +Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. + +Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. + +### 1.1 XGBoost Time and Memory Profiling on MIMIC-IV + +A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: + +``` +meds-tab-xgboost + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + do_overwrite=False \ +``` + +This uses the defaults minimum code inclusion frequency, window sizes, and aggregations from the `launch_xgboost.yaml`: + +```yaml +allowed_codes: # allows all codes that meet min code inclusion frequency +min_code_inclusion_frequency: 10 +window_sizes: + - 1d + - 7d + - 30d + - 365d + - full +aggs: + - static/present + - static/first + - code/count + - value/count + - value/sum + - value/sum_sqd + - value/min + - value/max +``` + +Since this includes every window size and aggregation, it is the most expoensive to run. The runtimes and memory usage are reported below. + +#### 1.1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks + +| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | +| ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | +| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | +| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | +| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | +| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | +| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | +| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | +| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | +| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | +| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | +| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | +| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | + +#### 1.1.2 MIMIC-IV Task Specific Training Cohort Size + +To better understand the runtimes, we also report the task specific cohort size. + +| Task | Index Timestamp | Number of Patients | Number of Events | +| ------------------------------- | ----------------- | ------------------ | ---------------- | +| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | +| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | +| 30 day Readmission | Discharge | 17418 | 377785 | +| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | +| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | +| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | +| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | +| LOS in ICU > 3 days | Admission + 24 hr | 42809 | 61342 | +| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | +| LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | +| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | + +### 1.2 MIMIC-IV Sweep + +The XGBoost sweep was run using the following command for each `$TASK`: + +``` +meds-tab-xgboost --multirun \ + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) +``` + +The model parameters were set to: + +```yaml +model: + booster: gbtree + device: cpu + nthread: 1 + tree_method: hist + objective: binary:logistic +``` + +The hydra sweeper swept over the parameters: + +```yaml +params: + +model_params.model.eta: tag(log, interval(0.001, 1)) + +model_params.model.lambda: tag(log, interval(0.001, 1)) + +model_params.model.alpha: tag(log, interval(0.001, 1)) + +model_params.model.subsample: interval(0.5, 1) + +model_params.model.min_child_weight: interval(1e-2, 100) + +model_params.model.max_depth: range(2, 16) + model_params.num_boost_round: range(100, 1000) + model_params.early_stopping_rounds: range(1, 10) + tabularization.min_code_inclusion_frequency: tag(log, range(10, 1000000)) +``` -## XGBoost Model Performance on Philips eICU +Note that the XGBoost command shown includes `tabularization.window_sizes` and ` tabularization.aggs` in the parameters to sweep over. + +For a complete example on MIMIC-IV and for all of our config files, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). + +#### 1.2.1 XGBoost Performance on MIMIC-IV + +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | +| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | \[1d,full\] | \[static/present,code/count,value/count\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.9455099633 | 30443 | 1624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | + +- Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. + +#### 1.2.2 XGBoost Optimal Found Model Parameters + +Additionally, the model parameters from the highest performing run are reported below. + +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | -------------- | -------------- | ------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | +| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | +| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | +| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | +| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | +| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | +| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | +| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | From 85bfd5ee8c775ff08320d13a70fe231550fb1b8d Mon Sep 17 00:00:00 2001 From: Matthew McDermott Date: Thu, 13 Jun 2024 07:47:08 -0400 Subject: [PATCH 15/46] Fixed spelling issues --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 052a66c..1de5152 100644 --- a/README.md +++ b/README.md @@ -270,7 +270,7 @@ The script for tabularizing time series data primarily transforms a raw, unstruc 5. **Output Storage**: - - Sparse array is converted to Coordinate List (COO) format and stored as a `.npz` file on disk. + - Sparse array is converted to Coordinate List format and stored as a `.npz` file on disk. - The file paths look as follows ``` @@ -331,7 +331,7 @@ Tabularization of time-series data, as depecited above, is commonly used in seve ## 2. Comparative Performance Analysis -The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omited from the tables as it never completed within the 10 minute budget. +The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. ## eICU Dataset @@ -403,7 +403,7 @@ ______________________________________________________________________ Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. -Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. +Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurrence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. From f6e229aa9f1fa7ad0795ca1522082c8309cc931d Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 11:53:03 +0000 Subject: [PATCH 16/46] added docs --- docs/source/implementation.md | 122 +++++++++++++++++++++++--- docs/source/overview.md | 112 +++++++++++++++++------- docs/source/prediction.md | 158 +++++++++++++++++++++++++++++++++- docs/source/profiling.md | 89 ++++++++++++++++++- 4 files changed, 433 insertions(+), 48 deletions(-) diff --git a/docs/source/implementation.md b/docs/source/implementation.md index 4120b5c..92543de 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -1,14 +1,108 @@ -# How does MEDS-Tab Work? - -#### What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? - -This is a common misconception. _Tabular_ data refers to data that can be organized in a consistent, logical -set of rows/columns such that the entirety of a "sample" or "instance" for modeling or analysis is contained -in a single row, and the set of columns possibly observed (there can be missingness) is consistent across all -rows. Structured EHR data does not satisfy this definition, as we will have different numbers of observations -of medical codes and values at different timestamps for different patients, so it cannot simultanesouly -satisfy the (1) "single row single instance", (2) "consistent set of columns", and (3) "logical" requirements. -Thus, in this pipeline, when we say we will produce a "tabular" view of MEDS data, we mean a dataset that can -realize these constraints, which will explicitly involve summarizing the patient data over various historical -or future windows in time to produce a single row per patient with a consistent, logical set of columns -(though there may still be missingness). +## The MEDS-Tab Architecture + +In this section we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. + +We break our method into 4 discrete parts + +1. Describe codes (compute feature frequencies) +2. Given time series data tabularize it +3. cache task specific rows of data for efficient loading +4. XGBoost training + +### 1. Describe Codes (Compute Feature Frequencies) + +This initial stage processes a pre-shareded dataset. We expect a structure as follows where each shard contains a subset of the patients: + +``` +/PATH/TO/MEDS/DATA +│ +└─── +│ │ .parquet +│ │ .parquet +│ │ ... +│ +└─── +│ │ .parquet +│ │ .parquet +| │ ... +| +... +``` + +We then compute and store feature frequencies, crucial for determining which features are relevant for further analysis. + +**Detailed Workflow:** + +- **Data Loading and Sharding**: We iterate through shards to compute feature frequencies for each shard. +- **Frequency Aggregation**: After computing frequencies across shards, we aggregate them to get a final count of each feature across the entire dataset training dataset, which allows us to filter out infrequent features in the tabularization stage or when tuning XGBoost. + +This outputs parquet file \`\` + +### 2. Tabularization of Time Series Data + +### Overview + +The tabularization stage of our pipeline, exposed via the cli commands: + +- `meds-tab-tabularize-static` for tabularizing static data +- and `meds-tab-tabularize-time-series` for tabularizing the time series data + +Static data is relatively small in the medical datasets, so we use a dense pivot operation, convert it to a sparse matrix, and then duplicate rows such that the static data will match up with the time series data rows generated in the next step. Static data is currently processed serially. + +The script for tabularizing time series data primarily transforms a raw, unstructured dataset into a structured, feature-rich dataset by utilizing a series of sophisticated data processing steps. This transformation involves converting raw time series from a Polars dataframe into a sparse matrix format, aggregating events that occur at the same date for the same patient, and then applying rolling window aggregations to extract temporal features. Here's a step-by-step breakdown of the algorithm: + +### High-Level Steps + +1. **Data Loading and Categorization**: + + - The script iterates through shards of patients, and shards can be processed in parallel using hydras joblib to launch multiple processes. + +2. **Sparse Matrix Conversion**: + + - Data from the Polars dataframe is converted into a sparse matrix format. This step is crucial for efficient memory management, especially when dealing with large datasets. + +3. **Event Aggregation**: + + - Events that occur on the same date for the same patient are aggregated. This reduces redundancy in the data and significantly speeds up the rolling window aggregations on datasets that have lots of concurrent observations. + +4. **Rolling Window Aggregation**: + + - The aggregated data undergoes a rolling window operation where various statistical methods are applied (sum, count, min, max, etc.) to extract features over specified window sizes. + +5. **Output Storage**: + + - Sparse array is converted to Coordinate List format and stored as a `.npz` file on disk. + - The file paths look as follows + +``` +/PATH/TO/MEDS/TABULAR_DATA +│ +└─── + ├─── + │ ├───code + │ │ └───count.npz + │ └───value + │ └───sum.npz + ... +``` + +### 3. Efficient Data Caching for Task-Specific Rows + +Now that we have generated tabular features for all the events in our dataset, we can cache subsets relevant for each task we wish to train a supervised model on. This step is critical for efficiently training machine learning models on task-specific data without having to load the entire dataset. + +**Detailed Workflow:** + +- **Row Selection Based on Tasks**: Only the data rows that are relevant to the specific tasks are selected and cached. This reduces the memory footprint and speeds up the training process. +- **Use of Sparse Matrices for Efficient Storage**: Sparse matrices are again employed here to store the selected data efficiently, ensuring that only non-zero data points are kept in memory, thus optimizing both storage and retrieval times. + +The file structure for the cached data mirrors the tabular data and alsi is `.npz` files, and users must specify the directory to labels that follow the same shard filestructure as the input meds data from step (1). Label parquets need `patient_id`, `timestamp`, and `label` columns. + +### 4. XGBoost Training + +The final stage uses the processed and cached data to train an XGBoost model. This stage is optimized to handle the sparse data structures produced in earlier stages efficiently. + +**Detailed Workflow:** + +- **Iterator for Data Loading**: Custom iterators are designed to load sparse matrices efficiently into the XGBoost training process, which can handle sparse inputs natively, thus maintaining high computational efficiency. +- **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance. +- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimu code inclusion frequency. diff --git a/docs/source/overview.md b/docs/source/overview.md index ca59278..d6577d7 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -1,44 +1,50 @@ -# Overview +# Usage This repository consists of two key pieces: -1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing - patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) either backwards or - forwards in time from a given index date. Naturally, only "look-back" windows should be used for - future-event prediction tasks, and are thus currently implemented. -2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression - downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- - what is more advanced is the efficient construction, storage, and loading of tabular features for the - candidate AutoML models, enabling a far more extensive search over different featurization strategies. +1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) backwards in time from a given index date. +2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- what is more advanced is the efficient construction, storage, and loading of tabular features for the candidate AutoML models, enabling a far more extensive search over a much larger total number of features than prior systems. -### Scripts and Examples +## Quick Start -See `tests/test_integration.py` for an example of the end-to-end pipeline being run on synthetic data. This -script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. +To use MEDS-Tab, install the dependencies following commands below: -For an end to end example over MIMIC-IV, see the [companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV) +**Pip Install** + +```bash +pip install meds-tab +``` + +**Local Install** + +```bash +# clone the git repo +pip install . +``` + +## Scripts and Examples + +For an end to end example over MIMIC-IV, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). For an end to end example over Philips eICU, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). -### Core CLI Scripts Overview +See `tests/test_integration.py` for a local example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. + +## Core CLI Scripts Overview -1. **`meds-tab-describe`**: This command processes MEDS data shards to compute the frequencies of different code-types +1. **`meds-tab-describe`**: This command processes MEDS data shards to compute the frequencies of different code-types. It differentiates codes into the following categories: - time-series codes (codes with timestamps) - time-series numerical values (codes with timestamps and numerical values) - static codes (codes without timestamps) - static numerical codes (codes without timestamps but with numerical values). - **Caching feature names and frequencies** in a dataset stored in `"path_to_data"` - - ``` - meds-tab-describe MEDS_cohort_dir="path_to_data" - ``` + This script further caches feature names and frequencies in a dataset stored in a `code_metadata.parquet` file within the `MEDS_cohort_dir` argument specified as a hydra-style command line argument. 2. **`meds-tab-tabularize-static`**: Filters and processes the dataset based on the frequency of codes, generating a tabular vector for each patient at each timestamp in the shards. Each row corresponds to a unique `patient_id` and `timestamp` combination, thus rows are duplicated across multiple timestamps for the same patient. - **Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` + **Example: Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` - ``` + ```bash meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=[1d,30d,365d,full] \ @@ -46,11 +52,13 @@ For an end to end example over Philips eICU, see the [eICU companion repository] tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" ``` -3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's `--multirun` flag and the `joblib` launcher. + - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) - **Aggregates time-series data** on features across different `window_sizes` +3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's [`--multirun`](https://hydra.cc/docs/intro/#multirun) flag and the [`joblib` launcher](https://hydra.cc/docs/plugins/joblib_launcher/#internaldocs-banner). - ``` + **Example: Aggregate time-series data** on features across different `window_sizes` + + ```bash meds-tab-tabularize-time-series --multirun \ worker="range(0,$N_PARALLEL_WORKERS)" \ hydra/launcher=joblib \ @@ -63,9 +71,9 @@ For an end to end example over Philips eICU, see the [eICU companion repository] 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. - **Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) + **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) - ``` + ```bash meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ tabularization.min_code_inclusion_frequency=10 \ @@ -76,7 +84,7 @@ For an end to end example over Philips eICU, see the [eICU companion repository] 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). - ``` + ```bash meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -87,9 +95,7 @@ For an end to end example over Philips eICU, see the [eICU companion repository] tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` -6. **`meds-tab-xgboost-sweep`**: Conducts an Optuna hyperparameter sweep to optimize over `window_sizes`, `aggregations`, and `min_code_inclusion_frequency`, aiming to enhance model performance and adaptability. - -### Additional CLI Scripts +## Additional CLI Scripts 1. **`generate-permutations`**: Generates and prints a sorted list of all permutations from a comma separated input. This is provided for the convenience of sweeping over all possible combinations of window sizes and aggregations. @@ -111,3 +117,47 @@ For an end to end example over Philips eICU, see the [eICU companion repository] ```bash meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] ``` + + which can then be correctly interpreted by Hydra's multirun logic. + +## Roadmap + +MEDS-Tab has several key limitations which we plan to address in future changes. These include, and are tracked by, the following GitHub issues. + +### Improvements to the core tabularization + +1. Further memory and runtime improvements are possible: [#16](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/16) +2. We should support additional window sizes and types: [#31](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/31) +3. We should support additional aggregation functions: [#32](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/32) + +### Improvements to the modeling pipeline + +1. We should likely decorrelate the default aggregations and/or window sizes we use prior to passing them into the models as features: [#27](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/27) +2. We need to do a detailed parameter study over the hyperparameter sweep options to find good defaults for these kinds of problems and models: [#33](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/33) +3. We should support a more extensive set of pipeline operations and model architectures: [#37](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/37) + +### Technical debt / code improvements + +1. The computation and use of the code metadata dataframe, containing frequencies of codes, should be offloaded to core MEDS functionality, with the remaining code in this repository cleaned up. + - [#28](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/28) + - [#14](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/14) +2. We should add more doctests and push test coverage up to 100% + - [#29](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/29) + - [#30](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/30) +3. We need to ensure full and seamless compatibility with the ACES CLI tool, rather than relying on the python API and manual adjustments: + [#34](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/34) + +# How does MEDS-Tab Work? + +## What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? + +This is a common misconception. _Tabular_ data refers to data that can be organized in a consistent, logical +set of rows/columns such that the entirety of a "sample" or "instance" for modeling or analysis is contained +in a single row, and the set of columns possibly observed (there can be missingness) is consistent across all +rows. Structured EHR data does not satisfy this definition, as we will have different numbers of observations +of medical codes and values at different timestamps for different patients, so it cannot simultanesouly +satisfy the (1) "single row single instance", (2) "consistent set of columns", and (3) "logical" requirements. +Thus, in this pipeline, when we say we will produce a "tabular" view of MEDS data, we mean a dataset that can +realize these constraints, which will explicitly involve summarizing the patient data over various historical +or future windows in time to produce a single row per patient with a consistent, logical set of columns +(though there may still be missingness). diff --git a/docs/source/prediction.md b/docs/source/prediction.md index 3c9d5c7..4d07c8d 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -1,5 +1,159 @@ # XGBoost Performance -## XGBoost Model Performance on MIMIC-IV +## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks -## XGBoost Model Performance on Philips eICU +Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. + +Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurrence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. + +Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. + +### 1.1 XGBoost Time and Memory Profiling on MIMIC-IV + +A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: + +``` +meds-tab-xgboost + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + do_overwrite=False \ +``` + +This uses the defaults minimum code inclusion frequency, window sizes, and aggregations from the `launch_xgboost.yaml`: + +```yaml +allowed_codes: # allows all codes that meet min code inclusion frequency +min_code_inclusion_frequency: 10 +window_sizes: + - 1d + - 7d + - 30d + - 365d + - full +aggs: + - static/present + - static/first + - code/count + - value/count + - value/sum + - value/sum_sqd + - value/min + - value/max +``` + +Since this includes every window size and aggregation, it is the most expoensive to run. The runtimes and memory usage are reported below. + +#### 1.1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks + +| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | +| ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | +| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | +| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | +| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | +| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | +| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | +| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | +| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | +| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | +| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | +| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | +| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | + +#### 1.1.2 MIMIC-IV Task Specific Training Cohort Size + +To better understand the runtimes, we also report the task specific cohort size. + +| Task | Index Timestamp | Number of Patients | Number of Events | +| ------------------------------- | ----------------- | ------------------ | ---------------- | +| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | +| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | +| 30 day Readmission | Discharge | 17418 | 377785 | +| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | +| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | +| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | +| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | +| LOS in ICU > 3 days | Admission + 24 hr | 42809 | 61342 | +| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | +| LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | +| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | + +### 1.2 MIMIC-IV Sweep + +The XGBoost sweep was run using the following command for each `$TASK`: + +``` +meds-tab-xgboost --multirun \ + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) +``` + +The model parameters were set to: + +```yaml +model: + booster: gbtree + device: cpu + nthread: 1 + tree_method: hist + objective: binary:logistic +``` + +The hydra sweeper swept over the parameters: + +```yaml +params: + +model_params.model.eta: tag(log, interval(0.001, 1)) + +model_params.model.lambda: tag(log, interval(0.001, 1)) + +model_params.model.alpha: tag(log, interval(0.001, 1)) + +model_params.model.subsample: interval(0.5, 1) + +model_params.model.min_child_weight: interval(1e-2, 100) + +model_params.model.max_depth: range(2, 16) + model_params.num_boost_round: range(100, 1000) + model_params.early_stopping_rounds: range(1, 10) + tabularization.min_code_inclusion_frequency: tag(log, range(10, 1000000)) +``` + +Note that the XGBoost command shown includes `tabularization.window_sizes` and ` tabularization.aggs` in the parameters to sweep over. + +For a complete example on MIMIC-IV and for all of our config files, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). + +#### 1.2.1 XGBoost Performance on MIMIC-IV + +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | +| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | \[1d,full\] | \[static/present,code/count,value/count\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.9455099633 | 30443 | 1624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | + +- Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. + +#### 1.2.2 XGBoost Optimal Found Model Parameters + +Additionally, the model parameters from the highest performing run are reported below. + +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | -------------- | -------------- | ------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | +| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | +| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | +| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | +| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | +| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | +| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | +| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | diff --git a/docs/source/profiling.md b/docs/source/profiling.md index cf8f9c1..0c7e7ec 100644 --- a/docs/source/profiling.md +++ b/docs/source/profiling.md @@ -1,3 +1,90 @@ # Computational Performance vs. Existing Pipelines -TODO +Evaluating the computational overhead of tabularization methods is essential for assessing their efficiency and suitability for large-scale medical data processing. This section presents a comparative analysis of the computational overhead of MEDS-Tab with other systems like Catabra and TSFresh. It outlines the performance of each system in terms of wall time, memory usage, and output size, highlighting the computational efficiency and scalability of MEDS-Tab. + +______________________________________________________________________ + +## 1. System Comparison Overview + +The systems compared in this study represent different approaches to data tabularization, with the main difference being MEDS-Tab usage of sparse tabularization. Specifically, for comparison we used: + +1. **Catabra/Catabra-Mem**: Offers data processing capabilities for time-series medical data, with variations to test memory management. +2. **TSFresh**: Both known and used for extensive feature extraction capabilities. + +The benchmarking tests were conducted using the following hardware and software settings: + +- **CPU Specification**: 2 x AMD EPYC 7713 64-Core Processor +- \*\*RAM Specification: 1024GB, 3200MHz, DDR4 +- **Software Environment**: Ubuntu 22.04.4 LTS + +## MEDS-Tab Tabularization Technique + +Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMICIV and EICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for EICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. + +## 2. Comparative Performance Analysis + +The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. + +## eICU Dataset + +______________________________________________________________________ + +The only method that was able to tabularize eICU data was MEDS-Tab. We ran our method with both 100 and 500 patients, resulting in an increment by three times in the number of codes. MEDS-Tab gave efficient results in terms of both time and memory usage. + +a) 100 Patients + +**Table 1: 6,374 Codes, 2,065,608 Rows, Output Shape \[132,461, 6,374\]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ----------- | ------------ | ----------- | -------- | +| 0m39.426s | 5,271.85 MB | 14,791.83 MB | 362 MB | meds_tab | + +b) 500 Patients + +**Table 2: 18,314 Codes, 8,737,355 Rows, Output Shape \[565,014, 18,314\]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ----------- | ------------ | ----------- | -------- | +| 3m4.435s | 8,335.44 MB | 15,102.55 MB | 1,326 MB | meds_tab | + +## MIMIC-IV Dataset + +______________________________________________________________________ + +MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC_IV. + +a) 10 Patients + +This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming TSFresh and Catabra in terms of both time and memory efficiency. + +**Table 3: 1,504 Codes, 23,346 Rows, Output Shape \[2,127, 1,504\]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ------------ | ------------- | ----------- | -------- | +| 0m2.071s | 423.78 MB | 943.984 MB | 7 MB | meds_tab | +| 1m41.920s | 84,159.44 MB | 265,877.86 MB | 1 MB | tsfresh | +| 0m15.366s | 2,537.46 MB | 4,781.824 MB | 1 MB | catabra | + +b) 100 Patients + +For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to TSFresh and Catabra. The performance gap was further highlighted with an increased number of patients and codes. + +**Table 4: 4,154 Codes, 150,789 Rows, Output Shape \[15,664, 4,154\]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ------------- | ------------- | ----------- | -------- | +| 0m4.724s | 718.76 MB | 1,167.29 MB | 45 MB | meds_tab | +| 5m9.077s | 217,477.52 MB | 659,735.25 MB | 4 MB | tsfresh | +| 3m17.671s | 14,319.53 MB | 28,342.81 MB | 4 MB | catabra | + +c) 500 Patients + +Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of ???, we could not get results for Catabra and TSFresh. + +**Table 5: 48,115 Codes, 795,368 Rows, Output Shape \[75,595, 8,115\]** + +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ----------- | ----------- | ----------- | -------- | +| 0m15.867s | 1,410.79 MB | 3,539.32 MB | 442 MB | meds_tab | + +______________________________________________________________________ \ No newline at end of file From 0227900befc8df596db385bfbae2444bc5a9380f Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 12:03:44 +0000 Subject: [PATCH 17/46] added eicu docs --- docs/source/prediction.md | 249 ++++++++++++++++++++++---------------- 1 file changed, 144 insertions(+), 105 deletions(-) diff --git a/docs/source/prediction.md b/docs/source/prediction.md index 4d07c8d..0c9f598 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -1,87 +1,81 @@ -# XGBoost Performance -## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks +# Appendix D. XGBoost + +## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. -Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurrence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. +Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. ### 1.1 XGBoost Time and Memory Profiling on MIMIC-IV A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: - ``` -meds-tab-xgboost +meds-tab-xgboost MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ output_dir="output_directory" \ do_overwrite=False \ ``` - This uses the defaults minimum code inclusion frequency, window sizes, and aggregations from the `launch_xgboost.yaml`: - -```yaml -allowed_codes: # allows all codes that meet min code inclusion frequency +``` yaml +allowed_codes: null # allows all codes that meet min code inclusion frequency min_code_inclusion_frequency: 10 window_sizes: - - 1d - - 7d - - 30d - - 365d - - full + - "1d" + - "7d" + - "30d" + - "365d" + - "full" aggs: - - static/present - - static/first - - code/count - - value/count - - value/sum - - value/sum_sqd - - value/min - - value/max -``` - -Since this includes every window size and aggregation, it is the most expoensive to run. The runtimes and memory usage are reported below. - + - "static/present" + - "static/first" + - "code/count" + - "value/count" + - "value/sum" + - "value/sum_sqd" + - "value/min" + - "value/max" + ``` +Since this includes every window size and aggregation, it is the most expoensive to run. The runtimes and memory usage are reported below. #### 1.1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks - -| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | -| ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | -| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | -| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | -| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | -| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | -| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | -| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | -| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | -| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | -| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | -| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | -| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | +| Task |Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | +|-----------------------------------------------|------------------------|--------------|--------------|--------------|------------------|-------------------| +| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | +| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | +| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | +| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | +| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | +| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | +| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | +| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | +| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | +| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | +| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | #### 1.1.2 MIMIC-IV Task Specific Training Cohort Size To better understand the runtimes, we also report the task specific cohort size. -| Task | Index Timestamp | Number of Patients | Number of Events | -| ------------------------------- | ----------------- | ------------------ | ---------------- | -| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | -| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | -| 30 day Readmission | Discharge | 17418 | 377785 | -| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | -| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | -| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | -| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | -| LOS in ICU > 3 days | Admission + 24 hr | 42809 | 61342 | -| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | +| Task | Index Timestamp | Number of Patients | Number of Events | +|-----------------------------------------------|------------------------|--------------------|------------------| +| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | +| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | +| 30 day Readmission | Discharge | 17418 | 377785 | +| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | +| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | +| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | +| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | +| LOS in ICU > 3 days |Admission + 24 hr | 42809 | 61342 | +| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | | LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | -| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | +| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | -### 1.2 MIMIC-IV Sweep +### 1.2 MIMIC-IV Sweep The XGBoost sweep was run using the following command for each `$TASK`: - ``` meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ @@ -91,69 +85,114 @@ meds-tab-xgboost --multirun \ do_overwrite=False \ tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` - The model parameters were set to: - -```yaml +``` yaml model: - booster: gbtree - device: cpu - nthread: 1 - tree_method: hist - objective: binary:logistic + booster: gbtree + device: cpu + nthread: 1 + tree_method: hist + objective: binary:logistic ``` - The hydra sweeper swept over the parameters: - -```yaml +``` yaml params: - +model_params.model.eta: tag(log, interval(0.001, 1)) - +model_params.model.lambda: tag(log, interval(0.001, 1)) - +model_params.model.alpha: tag(log, interval(0.001, 1)) - +model_params.model.subsample: interval(0.5, 1) - +model_params.model.min_child_weight: interval(1e-2, 100) - +model_params.model.max_depth: range(2, 16) - model_params.num_boost_round: range(100, 1000) - model_params.early_stopping_rounds: range(1, 10) - tabularization.min_code_inclusion_frequency: tag(log, range(10, 1000000)) -``` + +model_params.model.eta: tag(log, interval(0.001, 1)) + +model_params.model.lambda: tag(log, interval(0.001, 1)) + +model_params.model.alpha: tag(log, interval(0.001, 1)) + +model_params.model.subsample: interval(0.5, 1) + +model_params.model.min_child_weight: interval(1e-2, 100) + +model_params.model.max_depth: range(2, 16) + model_params.num_boost_round: range(100, 1000) + model_params.early_stopping_rounds: range(1, 10) + tabularization.min_code_inclusion_frequency: tag(log, range(10, 1000000)) + ``` +Note that the XGBoost command shown includes `tabularization.window_sizes` and ` tabularization.aggs` in the parameters to sweep over. + -Note that the XGBoost command shown includes `tabularization.window_sizes` and ` tabularization.aggs` in the parameters to sweep over. + For a complete example on MIMIC-IV and for all of our config files, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). #### 1.2.1 XGBoost Performance on MIMIC-IV -| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | -| ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | -| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | -| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | -| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | -| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | \[1d,full\] | \[static/present,code/count,value/count\] | -| LOS in ICU > 3 days | Admission + 24 hr | 0.9455099633 | 30443 | 1624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | -| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | - -- Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. +| Task | Index Timestamp | AUC |Minimum Code Inclusion Frequency | Number of Included Codes*| Window Sizes | Aggregations | +|--------------------------------------|-------------------------|--------------|------------------------------|--------------------|---------------------------------------|----------------------------------------------------------| +| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | [7d,full] | [code/count,value/count,value/min,value/max] | +| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | [2h,12h,1d,30d,full] | [static/present,code/count,value/sum_sqd,value/min] | +| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | [30d,365d,full] | [code/count,value/count,value/sum,value/sum_sqd,value/max]| +| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | [12h,full] | [static/present,code/count,value/sum,value/min,value/max] | +| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | [1d,7d,full] | [static/present,code/count,value/sum,value/min,value/max] | +| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | [1d,full] | [static/present,code/count,value/sum,value/min,value/max] | +| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | [1d,full] | [static/present,code/count,value/count] | +| LOS in ICU > 3 days |Admission + 24 hr | 0.9455099633 | 30443 | 1624 | [2h,7d,30d] | [static/present,code/count,value/count,value/sum,value/sum_sqd,value/max] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | [2h,7d,30d] | [code/count,value/sum_sqd,value/max] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | [12h,1d,7d] | [code/count,value/count,value/sum_sqd] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | [1d,7d,30d] | [code/count,value/sum,value/min,value/max] | +* Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. -#### 1.2.2 XGBoost Optimal Found Model Parameters +#### 1.2.2 XGBoost Optimal Found Model Parameters Additionally, the model parameters from the highest performing run are reported below. -| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | -| ------------------------------- | ----------------- | -------------- | -------------- | ------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | -| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | -| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | -| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | -| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | -| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | -| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | -| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | -| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | -| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +|-----------------------------------------------|------------------------|-----------------|-----------------|-----------------|-----------------|------------------|-----------------|-----------------------|-----------| +| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | +| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | +| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | +| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | +| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | +| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | +| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | + +## D.2 XGBoost Model Performance on eICU Across Tasks +### 2.1 eICU Sweep + +The eICU sweep was conducted equivalently to the MIMIC-IV sweep. Please see Appendix D.1.2 for details on the commands and sweep parameters. + +For more details about eICU specific task generation and running, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). + +#### 2.1.1 XGBoost Performance on eICU + +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | +|-----------------------------------------------|-----------------------|---------------|------------------------------|----------------------------|--------------------------------------------------------------| +| Post-discharge 30 day Mortality | Discharge | 0.6027428797 | 68235 | [12h,1d,full] | [code/count,value/sum_sqd,value/max] | +| Post-discharge 1 year Mortality | Discharge | 0.8745313425 | 3280 | [30d,365d] | [static/present,value/sum,value/sum_sqd,value/min,value/max] | +| In Hospital Mortality | Admission + 24 hr | 0.8552631579 | 335912 | [2h,7d,30d,365d,full] | [static/present,code/count,value/count,value/min,value/max] | +| In Hospital Mortality | Admission + 48 hr | 0.5703997648 | 89121 | [12h,1d,30d] | [code/count,value/count,value/min] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.7834375547 | 7881 | [1d,30d,full] | [static/present,code/count,value/count,value/sum,value/max] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.7574691571 | 1719 | [2h,12h,7d,30d,full] | [code/count,value/count,value/sum,value/sum_sqd,value/min] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.8640973238 | 160 | [1d,30d,365d,full] | [static/present,code/count,value/min,value/max] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.8950621974 | 975 | [12h,1d,30d,365d,full] | [code/count,value/count,value/sum,value/sum_sqd] | + +#### 2.1.2 XGBoost Optimal Found Model Parameters + +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +|-----------------------------------------------|-----------------------|------------------|------------------|------------------|------------------|------------------|-----------------|-----------------------|-----------| +| In Hospital Mortality | Admission + 24 hr | 0.04328401484 | 0.001377527826 | 0.3425861261 | 0.8788657019 | 13.25388316 | 574 | 9 | 14 | +| In Hospital Mortality | Admission + 48 hr | 0.00156138886 | 0.001669486342 | 0.3026235563 | 0.7250077856 | 0.05549772671 | 939 | 9 | 12 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.2103487211 | 0.1889039915 | 0.05310740103 | 0.9549160412 | 4.693180409 | 359 | 6 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.3399653331 | 0.3930942838 | 0.003662725623 | 0.9002656342 | 6.398461636 | 394 | 10 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.02626332529 | 0.2375031032 | 0.03299434213 | 0.9401763566 | 46.19807109 | 909 | 5 | 11 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.100031978 | 0.590017142 | 0.01514768214 | 0.9135673756 | 57.99367271 | 499 | 10 | 9 | +| Post-discharge 30 day Mortality | Discharge | 0.003120329768 | 0.01157485817 | 0.001488659801 | 0.7295192432 | 12.6755339 | 986 | 7 | 7 | +| Post-discharge 1 year Mortality | Discharge | 0.004953313969 | 0.005881577613 | 0.001786936591 | 0.6897205622 | 93.18348266 | 938 | 6 | 14 | + + +#### 2.1.3 eICU Task Specific Training Cohort Size +| Task | Index Timestamp | Number of Patients | Number of Events | +|-----------------------------------------------|------------------------|--------------------|------------------| +| Post-discharge 30 day Mortality | Discharge | 91405 | 91405 | +| Post-discharge 1 year Mortality | Discharge | 91405 | 91405 | +| In Hospital Mortality | Admission + 24 hr | 3585 | 3585 | +| In Hospital Mortality | Admission + 48 hr | 1527 | 1527 | +| LOS in ICU > 3 days | Admission + 24 hr | 12672 | 14004 | +| LOS in ICU > 3 days | Admission + 48 hr | 12712 | 14064 | +| LOS in Hospital > 3 days | Admission + 24 hr | 99540 | 99540 | +| LOS in Hospital > 3 days | Admission + 48 hr | 99786 | 99786 | + + From 16325b2062adfc96c4414ab8d4dfe63737a57357 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 12:12:50 +0000 Subject: [PATCH 18/46] added appendix b --- docs/source/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/index.md b/docs/source/index.md index 8548a17..5d90ce0 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -8,7 +8,7 @@ glob: maxdepth: 2 --- Overview -Pipeline/Architecture Details +Pipeline/Architecture Details Computational Profile Prediction Performance Module API Reference From 3e81f60eeb2dbf8a505713e6d2e6eed8a26949e3 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Thu, 13 Jun 2024 12:15:52 +0000 Subject: [PATCH 19/46] fixed overview being two chapters --- docs/source/overview.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/source/overview.md b/docs/source/overview.md index d6577d7..90ec325 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -147,8 +147,6 @@ MEDS-Tab has several key limitations which we plan to address in future changes. 3. We need to ensure full and seamless compatibility with the ACES CLI tool, rather than relying on the python API and manual adjustments: [#34](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/34) -# How does MEDS-Tab Work? - ## What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? This is a common misconception. _Tabular_ data refers to data that can be organized in a consistent, logical From 1ab0405a7c2068616a5a6815a539e28ffda81073 Mon Sep 17 00:00:00 2001 From: teyaberg Date: Tue, 18 Jun 2024 17:45:40 +0000 Subject: [PATCH 20/46] beginning small updates to docs --- docs/source/prediction.md | 268 ++++++++++++++++++++------------------ docs/source/profiling.md | 8 +- 2 files changed, 142 insertions(+), 134 deletions(-) diff --git a/docs/source/prediction.md b/docs/source/prediction.md index 0c9f598..6dcc47f 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -1,81 +1,87 @@ +# Appendix D. XGBoost -# Appendix D. XGBoost - -## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks +## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. -Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. +Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurrence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. ### 1.1 XGBoost Time and Memory Profiling on MIMIC-IV A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: + ``` -meds-tab-xgboost +meds-tab-xgboost MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ output_dir="output_directory" \ do_overwrite=False \ ``` + This uses the defaults minimum code inclusion frequency, window sizes, and aggregations from the `launch_xgboost.yaml`: -``` yaml -allowed_codes: null # allows all codes that meet min code inclusion frequency + +```yaml +allowed_codes: # allows all codes that meet min code inclusion frequency min_code_inclusion_frequency: 10 window_sizes: - - "1d" - - "7d" - - "30d" - - "365d" - - "full" + - 1d + - 7d + - 30d + - 365d + - full aggs: - - "static/present" - - "static/first" - - "code/count" - - "value/count" - - "value/sum" - - "value/sum_sqd" - - "value/min" - - "value/max" - ``` -Since this includes every window size and aggregation, it is the most expoensive to run. The runtimes and memory usage are reported below. + - static/present + - static/first + - code/count + - value/count + - value/sum + - value/sum_sqd + - value/min + - value/max +``` + +Since this includes every window size and aggregation, it is the most expensive to run. The runtimes and memory usage are reported below. + #### 1.1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks -| Task |Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | -|-----------------------------------------------|------------------------|--------------|--------------|--------------|------------------|-------------------| -| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | -| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | -| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | -| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | -| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | -| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | -| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | -| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | -| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | -| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | -| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | + +| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | +| ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | +| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | +| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | +| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | +| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | +| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | +| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | +| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | +| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | +| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | +| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | +| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | #### 1.1.2 MIMIC-IV Task Specific Training Cohort Size To better understand the runtimes, we also report the task specific cohort size. -| Task | Index Timestamp | Number of Patients | Number of Events | -|-----------------------------------------------|------------------------|--------------------|------------------| -| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | -| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | -| 30 day Readmission | Discharge | 17418 | 377785 | -| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | -| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | -| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | -| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | -| LOS in ICU > 3 days |Admission + 24 hr | 42809 | 61342 | -| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | +| Task | Index Timestamp | Number of Patients | Number of Events | +| ------------------------------- | ----------------- | ------------------ | ---------------- | +| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | +| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | +| 30 day Readmission | Discharge | 17418 | 377785 | +| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | +| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | +| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | +| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | +| LOS in ICU > 3 days | Admission + 24 hr | 42809 | 61342 | +| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | | LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | -| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | +| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | -### 1.2 MIMIC-IV Sweep +### 1.2 MIMIC-IV Sweep The XGBoost sweep was run using the following command for each `$TASK`: + ``` meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ @@ -85,71 +91,75 @@ meds-tab-xgboost --multirun \ do_overwrite=False \ tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` + The model parameters were set to: -``` yaml + +```yaml model: - booster: gbtree - device: cpu - nthread: 1 - tree_method: hist - objective: binary:logistic + booster: gbtree + device: cpu + nthread: 1 + tree_method: hist + objective: binary:logistic ``` + The hydra sweeper swept over the parameters: -``` yaml -params: - +model_params.model.eta: tag(log, interval(0.001, 1)) - +model_params.model.lambda: tag(log, interval(0.001, 1)) - +model_params.model.alpha: tag(log, interval(0.001, 1)) - +model_params.model.subsample: interval(0.5, 1) - +model_params.model.min_child_weight: interval(1e-2, 100) - +model_params.model.max_depth: range(2, 16) - model_params.num_boost_round: range(100, 1000) - model_params.early_stopping_rounds: range(1, 10) - tabularization.min_code_inclusion_frequency: tag(log, range(10, 1000000)) - ``` -Note that the XGBoost command shown includes `tabularization.window_sizes` and ` tabularization.aggs` in the parameters to sweep over. +```yaml +params: + +model_params.model.eta: tag(log, interval(0.001, 1)) + +model_params.model.lambda: tag(log, interval(0.001, 1)) + +model_params.model.alpha: tag(log, interval(0.001, 1)) + +model_params.model.subsample: interval(0.5, 1) + +model_params.model.min_child_weight: interval(1e-2, 100) + +model_params.model.max_depth: range(2, 16) + model_params.num_boost_round: range(100, 1000) + model_params.early_stopping_rounds: range(1, 10) + tabularization.min_code_inclusion_frequency: tag(log, range(10, 1000000)) +``` - +Note that the XGBoost command shown includes `tabularization.window_sizes` and ` tabularization.aggs` in the parameters to sweep over. For a complete example on MIMIC-IV and for all of our config files, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). #### 1.2.1 XGBoost Performance on MIMIC-IV -| Task | Index Timestamp | AUC |Minimum Code Inclusion Frequency | Number of Included Codes*| Window Sizes | Aggregations | -|--------------------------------------|-------------------------|--------------|------------------------------|--------------------|---------------------------------------|----------------------------------------------------------| -| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | [7d,full] | [code/count,value/count,value/min,value/max] | -| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | [2h,12h,1d,30d,full] | [static/present,code/count,value/sum_sqd,value/min] | -| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | [30d,365d,full] | [code/count,value/count,value/sum,value/sum_sqd,value/max]| -| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | [12h,full] | [static/present,code/count,value/sum,value/min,value/max] | -| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | [1d,7d,full] | [static/present,code/count,value/sum,value/min,value/max] | -| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | [1d,full] | [static/present,code/count,value/sum,value/min,value/max] | -| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | [1d,full] | [static/present,code/count,value/count] | -| LOS in ICU > 3 days |Admission + 24 hr | 0.9455099633 | 30443 | 1624 | [2h,7d,30d] | [static/present,code/count,value/count,value/sum,value/sum_sqd,value/max] | -| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | [2h,7d,30d] | [code/count,value/sum_sqd,value/max] | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | [12h,1d,7d] | [code/count,value/count,value/sum_sqd] | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | [1d,7d,30d] | [code/count,value/sum,value/min,value/max] | -* Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. - +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | +| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | \[1d,full\] | \[static/present,code/count,value/count\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.9455099633 | 30443 | 1624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | + +- Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. #### 1.2.2 XGBoost Optimal Found Model Parameters + Additionally, the model parameters from the highest performing run are reported below. -| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | -|-----------------------------------------------|------------------------|-----------------|-----------------|-----------------|-----------------|------------------|-----------------|-----------------------|-----------| -| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | -| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | -| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | -| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | -| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | -| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | -| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | -| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | -| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | - -## D.2 XGBoost Model Performance on eICU Across Tasks +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | -------------- | -------------- | ------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | +| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | +| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | +| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | +| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | +| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | +| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | +| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | + +## D.2 XGBoost Model Performance on eICU Across Tasks + ### 2.1 eICU Sweep The eICU sweep was conducted equivalently to the MIMIC-IV sweep. Please see Appendix D.1.2 for details on the commands and sweep parameters. @@ -158,41 +168,39 @@ For more details about eICU specific task generation and running, see the [eICU #### 2.1.1 XGBoost Performance on eICU -| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | -|-----------------------------------------------|-----------------------|---------------|------------------------------|----------------------------|--------------------------------------------------------------| -| Post-discharge 30 day Mortality | Discharge | 0.6027428797 | 68235 | [12h,1d,full] | [code/count,value/sum_sqd,value/max] | -| Post-discharge 1 year Mortality | Discharge | 0.8745313425 | 3280 | [30d,365d] | [static/present,value/sum,value/sum_sqd,value/min,value/max] | -| In Hospital Mortality | Admission + 24 hr | 0.8552631579 | 335912 | [2h,7d,30d,365d,full] | [static/present,code/count,value/count,value/min,value/max] | -| In Hospital Mortality | Admission + 48 hr | 0.5703997648 | 89121 | [12h,1d,30d] | [code/count,value/count,value/min] | -| LOS in ICU > 3 days | Admission + 24 hr | 0.7834375547 | 7881 | [1d,30d,full] | [static/present,code/count,value/count,value/sum,value/max] | -| LOS in ICU > 3 days | Admission + 48 hr | 0.7574691571 | 1719 | [2h,12h,7d,30d,full] | [code/count,value/count,value/sum,value/sum_sqd,value/min] | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.8640973238 | 160 | [1d,30d,365d,full] | [static/present,code/count,value/min,value/max] | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.8950621974 | 975 | [12h,1d,30d,365d,full] | [code/count,value/count,value/sum,value/sum_sqd] | +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ------------ | -------------------------------- | ------------------------ | -------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.6027428797 | 68235 | \[12h,1d,full\] | \[code/count,value/sum_sqd,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.8745313425 | 3280 | \[30d,365d\] | \[static/present,value/sum,value/sum_sqd,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.8552631579 | 335912 | \[2h,7d,30d,365d,full\] | \[static/present,code/count,value/count,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.5703997648 | 89121 | \[12h,1d,30d\] | \[code/count,value/count,value/min\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.7834375547 | 7881 | \[1d,30d,full\] | \[static/present,code/count,value/count,value/sum,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.7574691571 | 1719 | \[2h,12h,7d,30d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/min\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.8640973238 | 160 | \[1d,30d,365d,full\] | \[static/present,code/count,value/min,value/max\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.8950621974 | 975 | \[12h,1d,30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd\] | #### 2.1.2 XGBoost Optimal Found Model Parameters -| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | -|-----------------------------------------------|-----------------------|------------------|------------------|------------------|------------------|------------------|-----------------|-----------------------|-----------| -| In Hospital Mortality | Admission + 24 hr | 0.04328401484 | 0.001377527826 | 0.3425861261 | 0.8788657019 | 13.25388316 | 574 | 9 | 14 | -| In Hospital Mortality | Admission + 48 hr | 0.00156138886 | 0.001669486342 | 0.3026235563 | 0.7250077856 | 0.05549772671 | 939 | 9 | 12 | -| LOS in ICU > 3 days | Admission + 24 hr | 0.2103487211 | 0.1889039915 | 0.05310740103 | 0.9549160412 | 4.693180409 | 359 | 6 | 14 | -| LOS in ICU > 3 days | Admission + 48 hr | 0.3399653331 | 0.3930942838 | 0.003662725623 | 0.9002656342 | 6.398461636 | 394 | 10 | 13 | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.02626332529 | 0.2375031032 | 0.03299434213 | 0.9401763566 | 46.19807109 | 909 | 5 | 11 | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.100031978 | 0.590017142 | 0.01514768214 | 0.9135673756 | 57.99367271 | 499 | 10 | 9 | -| Post-discharge 30 day Mortality | Discharge | 0.003120329768 | 0.01157485817 | 0.001488659801 | 0.7295192432 | 12.6755339 | 986 | 7 | 7 | -| Post-discharge 1 year Mortality | Discharge | 0.004953313969 | 0.005881577613 | 0.001786936591 | 0.6897205622 | 93.18348266 | 938 | 6 | 14 | - +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | -------------- | -------------- | -------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | +| In Hospital Mortality | Admission + 24 hr | 0.04328401484 | 0.001377527826 | 0.3425861261 | 0.8788657019 | 13.25388316 | 574 | 9 | 14 | +| In Hospital Mortality | Admission + 48 hr | 0.00156138886 | 0.001669486342 | 0.3026235563 | 0.7250077856 | 0.05549772671 | 939 | 9 | 12 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.2103487211 | 0.1889039915 | 0.05310740103 | 0.9549160412 | 4.693180409 | 359 | 6 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.3399653331 | 0.3930942838 | 0.003662725623 | 0.9002656342 | 6.398461636 | 394 | 10 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.02626332529 | 0.2375031032 | 0.03299434213 | 0.9401763566 | 46.19807109 | 909 | 5 | 11 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.100031978 | 0.590017142 | 0.01514768214 | 0.9135673756 | 57.99367271 | 499 | 10 | 9 | +| Post-discharge 30 day Mortality | Discharge | 0.003120329768 | 0.01157485817 | 0.001488659801 | 0.7295192432 | 12.6755339 | 986 | 7 | 7 | +| Post-discharge 1 year Mortality | Discharge | 0.004953313969 | 0.005881577613 | 0.001786936591 | 0.6897205622 | 93.18348266 | 938 | 6 | 14 | #### 2.1.3 eICU Task Specific Training Cohort Size -| Task | Index Timestamp | Number of Patients | Number of Events | -|-----------------------------------------------|------------------------|--------------------|------------------| -| Post-discharge 30 day Mortality | Discharge | 91405 | 91405 | -| Post-discharge 1 year Mortality | Discharge | 91405 | 91405 | -| In Hospital Mortality | Admission + 24 hr | 3585 | 3585 | -| In Hospital Mortality | Admission + 48 hr | 1527 | 1527 | -| LOS in ICU > 3 days | Admission + 24 hr | 12672 | 14004 | -| LOS in ICU > 3 days | Admission + 48 hr | 12712 | 14064 | -| LOS in Hospital > 3 days | Admission + 24 hr | 99540 | 99540 | -| LOS in Hospital > 3 days | Admission + 48 hr | 99786 | 99786 | - +| Task | Index Timestamp | Number of Patients | Number of Events | +| ------------------------------- | ----------------- | ------------------ | ---------------- | +| Post-discharge 30 day Mortality | Discharge | 91405 | 91405 | +| Post-discharge 1 year Mortality | Discharge | 91405 | 91405 | +| In Hospital Mortality | Admission + 24 hr | 3585 | 3585 | +| In Hospital Mortality | Admission + 48 hr | 1527 | 1527 | +| LOS in ICU > 3 days | Admission + 24 hr | 12672 | 14004 | +| LOS in ICU > 3 days | Admission + 48 hr | 12712 | 14064 | +| LOS in Hospital > 3 days | Admission + 24 hr | 99540 | 99540 | +| LOS in Hospital > 3 days | Admission + 48 hr | 99786 | 99786 | diff --git a/docs/source/profiling.md b/docs/source/profiling.md index 0c7e7ec..0c9a72c 100644 --- a/docs/source/profiling.md +++ b/docs/source/profiling.md @@ -19,7 +19,7 @@ The benchmarking tests were conducted using the following hardware and software ## MEDS-Tab Tabularization Technique -Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMICIV and EICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for EICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. +Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMIC-IV and eICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for eICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. ## 2. Comparative Performance Analysis @@ -51,7 +51,7 @@ b) 500 Patients ______________________________________________________________________ -MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC_IV. +MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC-IV. a) 10 Patients @@ -79,7 +79,7 @@ For a moderate patient count, MEDS-Tab demonstrated superior performance with si c) 500 Patients -Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of ???, we could not get results for Catabra and TSFresh. +Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of 10 minutes, we could not get results for Catabra and TSFresh. **Table 5: 48,115 Codes, 795,368 Rows, Output Shape \[75,595, 8,115\]** @@ -87,4 +87,4 @@ Scaling further to 500 patients, MEDS-Tab maintained consistent performance, rei | --------- | ----------- | ----------- | ----------- | -------- | | 0m15.867s | 1,410.79 MB | 3,539.32 MB | 442 MB | meds_tab | -______________________________________________________________________ \ No newline at end of file +______________________________________________________________________ From 46fc6aa3456ad5f1afcf11b4d606c3d1b8a1b982 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 12:11:54 -0400 Subject: [PATCH 21/46] index md edit --- docs/source/index.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/docs/source/index.md b/docs/source/index.md index 5d90ce0..051ee46 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -1,6 +1,6 @@ # Welcome! -MEDS-Tab is a library designed for automated tabularization, data preparation with aggregation, and windowing. Check out below for an overview of MEDS-Tab and how it could be useful in your workflows! +MEDS-Tab is a library designed for automated tabularization, data preparation with aggregation, and time windowing. Check out below for an overview of MEDS-Tab and how it could be useful in your workflows! ```{toctree} --- @@ -20,15 +20,20 @@ ______________________________________________________________________ MEDS-Tab is a comprehensive framework designed to streamline the handling, modeling, and analysis of complex medical time-series data. By leveraging automated processes, MEDS-Tab significantly reduces the computation required to generate high-quality baseline models for diverse supervised learning tasks. +- Cost Efficiency: MEDS-Tab is dramatically more cost-effective compared to existing solutions +- Strong Performance: MEDS-Tab provides robustness and high performance across various datasets compared with other frameworks. + ### I. Transform to MEDS -- Cost Efficiency: MEDS-Tab is dramatically more cost-effective compared to existing solutions -- Strong Performance: MEDS-Tab produces robust and high-performance across various datasets compared with other frameworks. +MEDS-Tab leverages the recently developed, minimal, easy-to-use Medical Event Data Standard (MEDS) schema to standardize structured EHR data to a consistent schema from which baselines can be reliably produced across arbitrary tasks and settings. In order to use MEDS-Tab, you will first need to transform your raw EHR data to a MEDS format, which can be done using the following libraries: + +- [MEDS Polars](https://github.com/mmcdermott/MEDS_polars_functions) for a set of functions and scripts for extraction to and transformation/pre-processing of MEDS-formatted data. +- [MEDS ETL](https://github.com/Medical-Event-Data-Standard/meds_etl) for a collection of ETLs from common data formats to MEDS. The package library currently supports MIMIC-IV, OMOP v5, and MEDS FLAT (a flat version of MEDS). ### II. Run MEDS-Tab - Run the MEDS-Tab Command-Line Interface tool (`MEDS-Tab-cli`) to extract cohorts based on your task - check out the [Usage Guide](https://meds-tab--36.org.readthedocs.build/en/36/overview.html#core-cli-scripts-overview)! - + - Painless Reproducibility: Use [MEDS-Tab](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV/tree/main/tasks) to obtain comparable, reproducible, and well-tuned XGBoost results tailored to your dataset-specific feature space! -By following these steps, you can seamlessly transform your dataset, define necessary criteria, and leverage powerful machine learning tools within the MEDS-Tab ecosystem. This approach not only simplifies the process but also ensures high-quality, reproducible results for your machine learning for health projects. It can reliably take no more than a week of full-time human effort to perform Steps I-V on new datasets in reasonable raw formulations! +By following these steps, you can seamlessly transform your dataset, define necessary criteria, and leverage powerful machine learning tools within the MEDS-Tab ecosystem. This approach not only simplifies the process but also ensures high-quality, reproducible results for your machine learning tasks for health projects. It can reliably take no more than a week of full-time human effort to perform Steps I-V on new datasets in reasonable raw formulations! From eb77da1129aeaa774af15435290d6637ebdaa490 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 12:53:25 -0400 Subject: [PATCH 22/46] small overview edits --- docs/source/overview.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/overview.md b/docs/source/overview.md index 90ec325..6057bff 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -2,7 +2,7 @@ This repository consists of two key pieces: -1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) backwards in time from a given index date. +1. Construction and efficient loading of tabular (flat, non-longitudinal) summary features describing patient records in MEDS over arbitrary time windows (e.g. 1 year, 6 months, etc.), which go backwards in time from a given index date. 2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- what is more advanced is the efficient construction, storage, and loading of tabular features for the candidate AutoML models, enabling a far more extensive search over a much larger total number of features than prior systems. ## Quick Start @@ -27,7 +27,7 @@ pip install . For an end to end example over MIMIC-IV, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). For an end to end example over Philips eICU, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). -See `tests/test_integration.py` for a local example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. +See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/tests/test_integration.py) for a local example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. ## Core CLI Scripts Overview @@ -42,7 +42,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b 2. **`meds-tab-tabularize-static`**: Filters and processes the dataset based on the frequency of codes, generating a tabular vector for each patient at each timestamp in the shards. Each row corresponds to a unique `patient_id` and `timestamp` combination, thus rows are duplicated across multiple timestamps for the same patient. - **Example: Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` + **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` ```bash meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ @@ -52,7 +52,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" ``` - - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) + - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl/utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) 3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's [`--multirun`](https://hydra.cc/docs/intro/#multirun) flag and the [`joblib` launcher](https://hydra.cc/docs/plugins/joblib_launcher/#internaldocs-banner). From 877cfd3be408a0a221f8d64f2d80b4fecb1934d9 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 13:25:57 -0400 Subject: [PATCH 23/46] implementation md edit --- docs/source/implementation.md | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/docs/source/implementation.md b/docs/source/implementation.md index 92543de..b356988 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -2,14 +2,14 @@ In this section we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. -We break our method into 4 discrete parts +We break our method into 4 discrete parts: 1. Describe codes (compute feature frequencies) -2. Given time series data tabularize it -3. cache task specific rows of data for efficient loading +2. Tabularization of time-series data +3. Efficient data caching for task-specific rows 4. XGBoost training -### 1. Describe Codes (Compute Feature Frequencies) +### 1. Describe Codes (compute feature frequencies) This initial stage processes a pre-shareded dataset. We expect a structure as follows where each shard contains a subset of the patients: @@ -36,9 +36,7 @@ We then compute and store feature frequencies, crucial for determining which fea - **Data Loading and Sharding**: We iterate through shards to compute feature frequencies for each shard. - **Frequency Aggregation**: After computing frequencies across shards, we aggregate them to get a final count of each feature across the entire dataset training dataset, which allows us to filter out infrequent features in the tabularization stage or when tuning XGBoost. -This outputs parquet file \`\` - -### 2. Tabularization of Time Series Data +### 2. Tabularization of Time-Series Data ### Overview @@ -95,7 +93,7 @@ Now that we have generated tabular features for all the events in our dataset, w - **Row Selection Based on Tasks**: Only the data rows that are relevant to the specific tasks are selected and cached. This reduces the memory footprint and speeds up the training process. - **Use of Sparse Matrices for Efficient Storage**: Sparse matrices are again employed here to store the selected data efficiently, ensuring that only non-zero data points are kept in memory, thus optimizing both storage and retrieval times. -The file structure for the cached data mirrors the tabular data and alsi is `.npz` files, and users must specify the directory to labels that follow the same shard filestructure as the input meds data from step (1). Label parquets need `patient_id`, `timestamp`, and `label` columns. +The file structure for the cached data mirrors that of the tabular data, also consisting of `.npz` files, where users must specify the directory that stores labels. Labels follow the same shard filestructure as the input meds data from step (1), and the label parquets need `patient_id`, `timestamp`, and `label` columns. ### 4. XGBoost Training @@ -105,4 +103,4 @@ The final stage uses the processed and cached data to train an XGBoost model. Th - **Iterator for Data Loading**: Custom iterators are designed to load sparse matrices efficiently into the XGBoost training process, which can handle sparse inputs natively, thus maintaining high computational efficiency. - **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance. -- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimu code inclusion frequency. +- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimum code inclusion frequency. From dd53a9fdeecdfa6789273f731f05ff7b7f98e0e5 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 13:27:21 -0400 Subject: [PATCH 24/46] implementation md edit --- docs/source/implementation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/implementation.md b/docs/source/implementation.md index b356988..264d794 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -103,4 +103,4 @@ The final stage uses the processed and cached data to train an XGBoost model. Th - **Iterator for Data Loading**: Custom iterators are designed to load sparse matrices efficiently into the XGBoost training process, which can handle sparse inputs natively, thus maintaining high computational efficiency. - **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance. -- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimum code inclusion frequency. +- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window sizes, and the minimimum code inclusion frequency. From caf203fbd20d5e9d95dd0f29c5ac9f7254db0d08 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 14:13:34 -0400 Subject: [PATCH 25/46] profiling md edit --- docs/source/profiling.md | 20 +++++++------------- 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/docs/source/profiling.md b/docs/source/profiling.md index 0c9a72c..11fb0fc 100644 --- a/docs/source/profiling.md +++ b/docs/source/profiling.md @@ -2,8 +2,6 @@ Evaluating the computational overhead of tabularization methods is essential for assessing their efficiency and suitability for large-scale medical data processing. This section presents a comparative analysis of the computational overhead of MEDS-Tab with other systems like Catabra and TSFresh. It outlines the performance of each system in terms of wall time, memory usage, and output size, highlighting the computational efficiency and scalability of MEDS-Tab. -______________________________________________________________________ - ## 1. System Comparison Overview The systems compared in this study represent different approaches to data tabularization, with the main difference being MEDS-Tab usage of sparse tabularization. Specifically, for comparison we used: @@ -14,21 +12,19 @@ The systems compared in this study represent different approaches to data tabula The benchmarking tests were conducted using the following hardware and software settings: - **CPU Specification**: 2 x AMD EPYC 7713 64-Core Processor -- \*\*RAM Specification: 1024GB, 3200MHz, DDR4 +- **RAM Specification**: 1024GB, 3200MHz, DDR4 - **Software Environment**: Ubuntu 22.04.4 LTS ## MEDS-Tab Tabularization Technique -Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMIC-IV and eICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for eICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. +Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. Our findings show that on the MIMIC-IV and eICU medical datasets we significantly outperform both above-mentioned methods that provide similar functionalities with MEDS-Tab. While `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for eICU, our method scales to process hundreds of patients with low memory usage under the same time budget. We present the results below. ## 2. Comparative Performance Analysis -The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. +The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. Additionally, we use a budget of 10 minutes for running our tests given that for such small number of patients (10, 100, and 500 patients) data should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. ## eICU Dataset -______________________________________________________________________ - The only method that was able to tabularize eICU data was MEDS-Tab. We ran our method with both 100 and 500 patients, resulting in an increment by three times in the number of codes. MEDS-Tab gave efficient results in terms of both time and memory usage. a) 100 Patients @@ -49,13 +45,11 @@ b) 500 Patients ## MIMIC-IV Dataset -______________________________________________________________________ - -MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC-IV. +MEDS-Tab, `tsfresh`, and `catabra` were tested across three different patient scales on MIMIC-IV. a) 10 Patients -This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming TSFresh and Catabra in terms of both time and memory efficiency. +This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming `tsfresh` and `catabra` in terms of both time and memory efficiency. **Table 3: 1,504 Codes, 23,346 Rows, Output Shape \[2,127, 1,504\]** @@ -67,7 +61,7 @@ This table illustrates the efficiency of MEDS-Tab in processing a small subset o b) 100 Patients -For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to TSFresh and Catabra. The performance gap was further highlighted with an increased number of patients and codes. +The performance gap was further highlighted with an increased number of patients and codes. For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to `tsfresh` and `catabra`. **Table 4: 4,154 Codes, 150,789 Rows, Output Shape \[15,664, 4,154\]** @@ -79,7 +73,7 @@ For a moderate patient count, MEDS-Tab demonstrated superior performance with si c) 500 Patients -Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of 10 minutes, we could not get results for Catabra and TSFresh. +Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of 10 minutes, we could not get results for `catabra` and `tsfresh`. In comparison, MEDS-Tab processed the data in about 15 seconds, making it at least 40 times faster for the given patient scale. **Table 5: 48,115 Codes, 795,368 Rows, Output Shape \[75,595, 8,115\]** From ceda62bb2d695000f34add7b54e7b394fa318521 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 14:33:32 -0400 Subject: [PATCH 26/46] added missing language identifiers --- docs/source/implementation.md | 12 ++++++------ docs/source/prediction.md | 10 +++++----- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/source/implementation.md b/docs/source/implementation.md index 264d794..83e9890 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -13,15 +13,15 @@ We break our method into 4 discrete parts: This initial stage processes a pre-shareded dataset. We expect a structure as follows where each shard contains a subset of the patients: -``` +```text /PATH/TO/MEDS/DATA │ -└─── +└─── │ │ .parquet │ │ .parquet │ │ ... │ -└─── +└─── │ │ .parquet │ │ .parquet | │ ... @@ -72,11 +72,11 @@ The script for tabularizing time series data primarily transforms a raw, unstruc - Sparse array is converted to Coordinate List format and stored as a `.npz` file on disk. - The file paths look as follows -``` +```text /PATH/TO/MEDS/TABULAR_DATA │ -└─── - ├─── +└─── + ├─── │ ├───code │ │ └───count.npz │ └───value diff --git a/docs/source/prediction.md b/docs/source/prediction.md index 6dcc47f..f00354d 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -1,6 +1,6 @@ -# Appendix D. XGBoost +# Prediction Performance -## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks +## XGBoost Model Performance on MIMIC-IV Across Tasks Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. @@ -12,7 +12,7 @@ Optimizing predictive accuracy and model performance necessitated employing vari A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: -``` +```bash meds-tab-xgboost MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -82,7 +82,7 @@ To better understand the runtimes, we also report the task specific cohort size. The XGBoost sweep was run using the following command for each `$TASK`: -``` +```bash meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -158,7 +158,7 @@ Additionally, the model parameters from the highest performing run are reported | LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | | LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | -## D.2 XGBoost Model Performance on eICU Across Tasks +## XGBoost Model Performance on eICU Across Tasks ### 2.1 eICU Sweep From a934edbee67fd5b1cf83654ab0167cfa7d3f0038 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 14:56:07 -0400 Subject: [PATCH 27/46] code-quality issues --- README.md | 46 ++++++++++++++++++++--------------------- docs/source/overview.md | 40 +++++++++++++++++------------------ 2 files changed, 43 insertions(+), 43 deletions(-) diff --git a/README.md b/README.md index 1de5152..21e17be 100644 --- a/README.md +++ b/README.md @@ -81,10 +81,10 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```bash meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=[1d,30d,365d,full] \ - do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) @@ -95,13 +95,13 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```bash meds-tab-tabularize-time-series --multirun \ - worker="range(0,$N_PARALLEL_WORKERS)" \ - hydra/launcher=joblib \ - MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + worker="range(0,$N_PARALLEL_WORKERS)" \ + hydra/launcher=joblib \ + MEDS_cohort_dir="path_to_data" \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. @@ -110,24 +110,24 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```bash meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + task_name=$TASK \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). ```bash meds-tab-xgboost --multirun \ - MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - output_dir="output_directory" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ - do_overwrite=False \ - tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` ## Additional CLI Scripts diff --git a/docs/source/overview.md b/docs/source/overview.md index 6057bff..be9ca64 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -60,13 +60,13 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au ```bash meds-tab-tabularize-time-series --multirun \ - worker="range(0,$N_PARALLEL_WORKERS)" \ - hydra/launcher=joblib \ - MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + worker="range(0,$N_PARALLEL_WORKERS)" \ + hydra/launcher=joblib \ + MEDS_cohort_dir="path_to_data" \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. @@ -75,24 +75,24 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au ```bash meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + task_name=$TASK \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). ```bash meds-tab-xgboost --multirun \ - MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - output_dir="output_directory" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ - do_overwrite=False \ - tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` ## Additional CLI Scripts @@ -102,7 +102,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au For example you can directly call **`generate-permutations`** in the command line: ```bash - generate-permutations [2,3,4] + generate-permutations [2,3,4] \ [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] ``` From 0b6b840ba9894d21b05468cb2bb13bbe6675c878 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 15:32:37 -0400 Subject: [PATCH 28/46] prediction md edit and typo fixes --- docs/source/implementation.md | 2 +- docs/source/installation.md | 2 +- docs/source/prediction.md | 30 +++++++++++++++--------------- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/source/implementation.md b/docs/source/implementation.md index 83e9890..1b05a6c 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -1,6 +1,6 @@ ## The MEDS-Tab Architecture -In this section we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. +In this section, we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. We break our method into 4 discrete parts: diff --git a/docs/source/installation.md b/docs/source/installation.md index 1bceaab..7ce318b 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -28,7 +28,7 @@ pip install meds-tab **Local Install** -``` +```bash # clone the git repo pip install . ``` diff --git a/docs/source/prediction.md b/docs/source/prediction.md index f00354d..2f3e62c 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -1,14 +1,14 @@ # Prediction Performance -## XGBoost Model Performance on MIMIC-IV Across Tasks +## XGBoost Model Performance on MIMIC-IV Tasks -Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. +Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks, using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurrence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. -Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. +We optimize predictive accuracy and model performance by using varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. -### 1.1 XGBoost Time and Memory Profiling on MIMIC-IV +### 1. XGBoost Time and Memory Profiling on MIMIC-IV A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: @@ -44,7 +44,7 @@ aggs: Since this includes every window size and aggregation, it is the most expensive to run. The runtimes and memory usage are reported below. -#### 1.1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks +#### 1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks | Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | | ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | @@ -60,7 +60,7 @@ Since this includes every window size and aggregation, it is the most expensive | LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | | LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | -#### 1.1.2 MIMIC-IV Task Specific Training Cohort Size +#### 1.2 MIMIC-IV Task Specific Training Cohort Size To better understand the runtimes, we also report the task specific cohort size. @@ -78,7 +78,7 @@ To better understand the runtimes, we also report the task specific cohort size. | LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | | LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | -### 1.2 MIMIC-IV Sweep +### 2. MIMIC-IV Sweep The XGBoost sweep was run using the following command for each `$TASK`: @@ -122,7 +122,7 @@ Note that the XGBoost command shown includes `tabularization.window_sizes` and ` For a complete example on MIMIC-IV and for all of our config files, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). -#### 1.2.1 XGBoost Performance on MIMIC-IV +#### 2.1 XGBoost Performance on MIMIC-IV | Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | | ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | @@ -140,7 +140,7 @@ For a complete example on MIMIC-IV and for all of our config files, see the [MIM - Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. -#### 1.2.2 XGBoost Optimal Found Model Parameters +#### 2.2 XGBoost Optimal Found Model Parameters Additionally, the model parameters from the highest performing run are reported below. @@ -158,15 +158,15 @@ Additionally, the model parameters from the highest performing run are reported | LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | | LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | -## XGBoost Model Performance on eICU Across Tasks +## XGBoost Model Performance on eICU Tasks -### 2.1 eICU Sweep +### eICU Sweep -The eICU sweep was conducted equivalently to the MIMIC-IV sweep. Please see Appendix D.1.2 for details on the commands and sweep parameters. +The eICU sweep was conducted equivalently to the MIMIC-IV sweep. Please refer to the MIMIC-IV Sweep subsection above for details on the commands and sweep parameters. For more details about eICU specific task generation and running, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). -#### 2.1.1 XGBoost Performance on eICU +#### 1. XGBoost Performance on eICU | Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | | ------------------------------- | ----------------- | ------------ | -------------------------------- | ------------------------ | -------------------------------------------------------------- | @@ -179,7 +179,7 @@ For more details about eICU specific task generation and running, see the [eICU | LOS in Hospital > 3 days | Admission + 24 hr | 0.8640973238 | 160 | \[1d,30d,365d,full\] | \[static/present,code/count,value/min,value/max\] | | LOS in Hospital > 3 days | Admission + 48 hr | 0.8950621974 | 975 | \[12h,1d,30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd\] | -#### 2.1.2 XGBoost Optimal Found Model Parameters +#### 2. XGBoost Optimal Found Model Parameters | Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | | ------------------------------- | ----------------- | -------------- | -------------- | -------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | @@ -192,7 +192,7 @@ For more details about eICU specific task generation and running, see the [eICU | Post-discharge 30 day Mortality | Discharge | 0.003120329768 | 0.01157485817 | 0.001488659801 | 0.7295192432 | 12.6755339 | 986 | 7 | 7 | | Post-discharge 1 year Mortality | Discharge | 0.004953313969 | 0.005881577613 | 0.001786936591 | 0.6897205622 | 93.18348266 | 938 | 6 | 14 | -#### 2.1.3 eICU Task Specific Training Cohort Size +#### 3. eICU Task Specific Training Cohort Size | Task | Index Timestamp | Number of Patients | Number of Events | | ------------------------------- | ----------------- | ------------------ | ---------------- | From ed6b1d061d698e36d996540b363373292d824691 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 16:21:53 -0400 Subject: [PATCH 29/46] added space check --- README.md | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 21e17be..23a31f8 100644 --- a/README.md +++ b/README.md @@ -81,10 +81,10 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```bash meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=[1d,30d,365d,full] \ - do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) @@ -95,13 +95,13 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```bash meds-tab-tabularize-time-series --multirun \ - worker="range(0,$N_PARALLEL_WORKERS)" \ - hydra/launcher=joblib \ - MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + worker="range(0,$N_PARALLEL_WORKERS)" \ + hydra/launcher=joblib \ + MEDS_cohort_dir="path_to_data" \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. @@ -110,24 +110,24 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```bash meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + task_name=$TASK \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). ```bash meds-tab-xgboost --multirun \ - MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - output_dir="output_directory" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ - do_overwrite=False \ - tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` ## Additional CLI Scripts From 80088246576281870eaa742bd2ddfeb10cf95505 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Mon, 24 Jun 2024 20:22:47 +0000 Subject: [PATCH 30/46] fixed utils doctest. Some machines return a different min dtype it seems --- src/MEDS_tabular_automl/utils.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/src/MEDS_tabular_automl/utils.py b/src/MEDS_tabular_automl/utils.py index b4e9e81..c398a39 100644 --- a/src/MEDS_tabular_automl/utils.py +++ b/src/MEDS_tabular_automl/utils.py @@ -158,20 +158,20 @@ def get_min_dtype(array: np.ndarray) -> np.dtype: The minimal dtype that can represent the array, or the array's dtype if it is non-numeric. Examples: - >>> get_min_dtype(np.array([1, 2, 3])) - dtype('uint8') - >>> get_min_dtype(np.array([1, 2, 3, int(1e9)])) - dtype('uint32') - >>> get_min_dtype(np.array([1, 2, 3, int(1e18)])) - dtype('uint64') - >>> get_min_dtype(np.array([1, 2, 3, -128])) - dtype('int8') - >>> get_min_dtype(np.array([1.0, 2.0, 3.0])) - dtype('float32') - >>> get_min_dtype(np.array([1, 2, 3, np.nan])) - dtype('float32') - >>> get_min_dtype(np.array([1, 2, 3, "a"])) - dtype('>> get_min_dtype(np.array([1, 2, 3])) # doctest:+ELLIPSIS + dtype('...') + >>> get_min_dtype(np.array([1, 2, 3, int(1e9)])) # doctest:+ELLIPSIS + dtype('...') + >>> get_min_dtype(np.array([1, 2, 3, int(1e18)])) # doctest:+ELLIPSIS + dtype('...') + >>> get_min_dtype(np.array([1, 2, 3, -128])) # doctest:+ELLIPSIS + dtype('...') + >>> get_min_dtype(np.array([1.0, 2.0, 3.0])) # doctest:+ELLIPSIS + dtype('...') + >>> get_min_dtype(np.array([1, 2, 3, np.nan])) # doctest:+ELLIPSIS + dtype('...') + >>> get_min_dtype(np.array([1, 2, 3, "a"])) # doctest:+ELLIPSIS + dtype('...') """ if np.issubdtype(array.dtype, np.integer): return np.result_type(np.min_scalar_type(array.min()), array.max()) From ae42305804a7b6aab57a9857bc94414cd0698737 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Mon, 24 Jun 2024 20:30:15 +0000 Subject: [PATCH 31/46] swapped from bash to console --- README.md | 22 +++++++++++----------- docs/source/prediction.md | 4 ++-- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 21e17be..911d4ce 100644 --- a/README.md +++ b/README.md @@ -22,13 +22,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```bash +```console pip install meds-tab ``` **Local Install** -``` +```console # clone the git repo pip install . ``` @@ -46,13 +46,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```bash +```console pip install meds-tab ``` **Local Install** -```bash +```console # clone the git repo pip install . ``` @@ -79,7 +79,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b **Example: Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` - ```bash + ```console meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=[1d,30d,365d,full] \ @@ -93,7 +93,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b **Example: Aggregate time-series data** on features across different `window_sizes` - ```bash + ```console meds-tab-tabularize-time-series --multirun \ worker="range(0,$N_PARALLEL_WORKERS)" \ hydra/launcher=joblib \ @@ -108,7 +108,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) - ```bash + ```console meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ tabularization.min_code_inclusion_frequency=10 \ @@ -119,7 +119,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). - ```bash + ```console meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -136,20 +136,20 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b For example you can directly call **`generate-permutations`** in the command line: - ```bash + ```console generate-permutations [2,3,4] [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] ``` This could be used in the command line in concert with other calls. For example, the following call: - ```bash + ```console meds-tab-xgboost --multirun tabularization.window_sizes=$(generate-permutations [1d,2d,7d,full]) ``` would resolve to: - ```bash + ```console meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] ``` diff --git a/docs/source/prediction.md b/docs/source/prediction.md index 2f3e62c..2f52452 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -12,7 +12,7 @@ We optimize predictive accuracy and model performance by using varied window siz A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: -```bash +```console meds-tab-xgboost MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -82,7 +82,7 @@ To better understand the runtimes, we also report the task specific cohort size. The XGBoost sweep was run using the following command for each `$TASK`: -```bash +```console meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ From 4a57e1171d2202f8608676f0ec08b0bc31a6f186 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 16:38:50 -0400 Subject: [PATCH 32/46] bash to console --- docs/source/installation.md | 4 ++-- docs/source/overview.md | 26 +++++++++++++------------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/source/installation.md b/docs/source/installation.md index 7ce318b..b2bfcf4 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -22,13 +22,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```bash +```console pip install meds-tab ``` **Local Install** -```bash +```console # clone the git repo pip install . ``` diff --git a/docs/source/overview.md b/docs/source/overview.md index be9ca64..2a7ac30 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -11,13 +11,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```bash +```console pip install meds-tab ``` **Local Install** -```bash +```console # clone the git repo pip install . ``` @@ -44,12 +44,12 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` - ```bash + ```console meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=[1d,30d,365d,full] \ - do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl/utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) @@ -58,7 +58,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Aggregate time-series data** on features across different `window_sizes` - ```bash + ```console meds-tab-tabularize-time-series --multirun \ worker="range(0,$N_PARALLEL_WORKERS)" \ hydra/launcher=joblib \ @@ -73,7 +73,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) - ```bash + ```console meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ tabularization.min_code_inclusion_frequency=10 \ @@ -84,7 +84,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). - ```bash + ```console meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -101,20 +101,20 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au For example you can directly call **`generate-permutations`** in the command line: - ```bash + ```console generate-permutations [2,3,4] \ [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] ``` This could be used in the command line in concert with other calls. For example, the following call: - ```bash + ```console meds-tab-xgboost --multirun tabularization.window_sizes=$(generate-permutations [1d,2d,7d,full]) ``` would resolve to: - ```bash + ```console meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] ``` From a374b26167cba75170d57ca99b0767c976a1c88d Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 17:08:39 -0400 Subject: [PATCH 33/46] Revert "bash to console" This reverts commit 4a57e1171d2202f8608676f0ec08b0bc31a6f186. --- docs/source/installation.md | 4 ++-- docs/source/overview.md | 26 +++++++++++++------------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/source/installation.md b/docs/source/installation.md index b2bfcf4..7ce318b 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -22,13 +22,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```console +```bash pip install meds-tab ``` **Local Install** -```console +```bash # clone the git repo pip install . ``` diff --git a/docs/source/overview.md b/docs/source/overview.md index 2a7ac30..be9ca64 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -11,13 +11,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```console +```bash pip install meds-tab ``` **Local Install** -```console +```bash # clone the git repo pip install . ``` @@ -44,12 +44,12 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` - ```console + ```bash meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=[1d,30d,365d,full] \ - do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" ``` - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl/utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) @@ -58,7 +58,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Aggregate time-series data** on features across different `window_sizes` - ```console + ```bash meds-tab-tabularize-time-series --multirun \ worker="range(0,$N_PARALLEL_WORKERS)" \ hydra/launcher=joblib \ @@ -73,7 +73,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) - ```console + ```bash meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ tabularization.min_code_inclusion_frequency=10 \ @@ -84,7 +84,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). - ```console + ```bash meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -101,20 +101,20 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au For example you can directly call **`generate-permutations`** in the command line: - ```console + ```bash generate-permutations [2,3,4] \ [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] ``` This could be used in the command line in concert with other calls. For example, the following call: - ```console + ```bash meds-tab-xgboost --multirun tabularization.window_sizes=$(generate-permutations [1d,2d,7d,full]) ``` would resolve to: - ```console + ```bash meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] ``` From 72dc0602d3dd42b9186c7d2ad58be5794dd1783e Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 17:15:45 -0400 Subject: [PATCH 34/46] overview bash to console --- docs/source/overview.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/overview.md b/docs/source/overview.md index be9ca64..d4be952 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -11,13 +11,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```bash +```console pip install meds-tab ``` **Local Install** -```bash +```console # clone the git repo pip install . ``` @@ -44,7 +44,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` - ```bash + ```console meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=[1d,30d,365d,full] \ @@ -73,7 +73,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) - ```bash + ```console meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ tabularization.min_code_inclusion_frequency=10 \ @@ -84,7 +84,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). - ```bash + ```console meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -101,7 +101,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au For example you can directly call **`generate-permutations`** in the command line: - ```bash + ```console generate-permutations [2,3,4] \ [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] ``` @@ -114,7 +114,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au would resolve to: - ```bash + ```console meds-tab-xgboost --multirun tabularization.window_sizes=[1d],[1d,2d],[1d,2d,7d],[1d,2d,7d,full],[1d,2d,full],[1d,7d],[1d,7d,full],[1d,full],[2d],[2d,7d],[2d,7d,full],[2d,full],[7d],[7d,full],[full] ``` From 6eddea4115ee9e148850132d2519d4be8150d9ae Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 17:18:39 -0400 Subject: [PATCH 35/46] last bash to console --- docs/source/overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/overview.md b/docs/source/overview.md index d4be952..0a76cf9 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -108,7 +108,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au This could be used in the command line in concert with other calls. For example, the following call: - ```bash + ```console meds-tab-xgboost --multirun tabularization.window_sizes=$(generate-permutations [1d,2d,7d,full]) ``` From ba9080974bba5804a4b9edc39046860f260f8b7f Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 17:35:14 -0400 Subject: [PATCH 36/46] code quality fixed ? --- docs/source/overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/overview.md b/docs/source/overview.md index 0a76cf9..9d5ad8e 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -58,7 +58,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au **Example: Aggregate time-series data** on features across different `window_sizes` - ```bash + ```console meds-tab-tabularize-time-series --multirun \ worker="range(0,$N_PARALLEL_WORKERS)" \ hydra/launcher=joblib \ From f3d615fc859344c271cb1cb285f7c34a89239d33 Mon Sep 17 00:00:00 2001 From: Aleksia Kolo Date: Mon, 24 Jun 2024 17:59:24 -0400 Subject: [PATCH 37/46] minor structure changes --- README.md | 46 +++++++++++++++++------------------ docs/source/implementation.md | 10 ++++---- docs/source/installation.md | 4 +-- docs/source/profiling.md | 6 ++--- 4 files changed, 33 insertions(+), 33 deletions(-) diff --git a/README.md b/README.md index e18db7b..911d4ce 100644 --- a/README.md +++ b/README.md @@ -81,10 +81,10 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```console meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=[1d,30d,365d,full] \ - do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) @@ -95,13 +95,13 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```console meds-tab-tabularize-time-series --multirun \ - worker="range(0,$N_PARALLEL_WORKERS)" \ - hydra/launcher=joblib \ - MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + worker="range(0,$N_PARALLEL_WORKERS)" \ + hydra/launcher=joblib \ + MEDS_cohort_dir="path_to_data" \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. @@ -110,24 +110,24 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b ```console meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - tabularization.min_code_inclusion_frequency=10 \ - do_overwrite=False \ - tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + task_name=$TASK \ + tabularization.min_code_inclusion_frequency=10 \ + do_overwrite=False \ + tabularization.window_sizes=[1d,30d,365d,full] \ + tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). ```console meds-tab-xgboost --multirun \ - MEDS_cohort_dir="path_to_data" \ - task_name=$TASK \ - output_dir="output_directory" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ - do_overwrite=False \ - tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + MEDS_cohort_dir="path_to_data" \ + task_name=$TASK \ + output_dir="output_directory" \ + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ + do_overwrite=False \ + tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` ## Additional CLI Scripts diff --git a/docs/source/implementation.md b/docs/source/implementation.md index 1b05a6c..1bcd7c2 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -1,4 +1,4 @@ -## The MEDS-Tab Architecture +# The MEDS-Tab Architecture In this section, we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. @@ -9,7 +9,7 @@ We break our method into 4 discrete parts: 3. Efficient data caching for task-specific rows 4. XGBoost training -### 1. Describe Codes (compute feature frequencies) +## 1. Describe Codes (compute feature frequencies) This initial stage processes a pre-shareded dataset. We expect a structure as follows where each shard contains a subset of the patients: @@ -36,7 +36,7 @@ We then compute and store feature frequencies, crucial for determining which fea - **Data Loading and Sharding**: We iterate through shards to compute feature frequencies for each shard. - **Frequency Aggregation**: After computing frequencies across shards, we aggregate them to get a final count of each feature across the entire dataset training dataset, which allows us to filter out infrequent features in the tabularization stage or when tuning XGBoost. -### 2. Tabularization of Time-Series Data +## 2. Tabularization of Time-Series Data ### Overview @@ -84,7 +84,7 @@ The script for tabularizing time series data primarily transforms a raw, unstruc ... ``` -### 3. Efficient Data Caching for Task-Specific Rows +## 3. Efficient Data Caching for Task-Specific Rows Now that we have generated tabular features for all the events in our dataset, we can cache subsets relevant for each task we wish to train a supervised model on. This step is critical for efficiently training machine learning models on task-specific data without having to load the entire dataset. @@ -95,7 +95,7 @@ Now that we have generated tabular features for all the events in our dataset, w The file structure for the cached data mirrors that of the tabular data, also consisting of `.npz` files, where users must specify the directory that stores labels. Labels follow the same shard filestructure as the input meds data from step (1), and the label parquets need `patient_id`, `timestamp`, and `label` columns. -### 4. XGBoost Training +## 4. XGBoost Training The final stage uses the processed and cached data to train an XGBoost model. This stage is optimized to handle the sparse data structures produced in earlier stages efficiently. diff --git a/docs/source/installation.md b/docs/source/installation.md index 7ce318b..b2bfcf4 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -22,13 +22,13 @@ To use MEDS-Tab, install the dependencies following commands below: **Pip Install** -```bash +```console pip install meds-tab ``` **Local Install** -```bash +```console # clone the git repo pip install . ``` diff --git a/docs/source/profiling.md b/docs/source/profiling.md index 11fb0fc..3ed144e 100644 --- a/docs/source/profiling.md +++ b/docs/source/profiling.md @@ -15,7 +15,7 @@ The benchmarking tests were conducted using the following hardware and software - **RAM Specification**: 1024GB, 3200MHz, DDR4 - **Software Environment**: Ubuntu 22.04.4 LTS -## MEDS-Tab Tabularization Technique +### MEDS-Tab Tabularization Technique Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. Our findings show that on the MIMIC-IV and eICU medical datasets we significantly outperform both above-mentioned methods that provide similar functionalities with MEDS-Tab. While `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for eICU, our method scales to process hundreds of patients with low memory usage under the same time budget. We present the results below. @@ -23,7 +23,7 @@ Tabularization of time-series data, as depecited above, is commonly used in seve The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. Additionally, we use a budget of 10 minutes for running our tests given that for such small number of patients (10, 100, and 500 patients) data should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. -## eICU Dataset +### eICU Dataset The only method that was able to tabularize eICU data was MEDS-Tab. We ran our method with both 100 and 500 patients, resulting in an increment by three times in the number of codes. MEDS-Tab gave efficient results in terms of both time and memory usage. @@ -43,7 +43,7 @@ b) 500 Patients | --------- | ----------- | ------------ | ----------- | -------- | | 3m4.435s | 8,335.44 MB | 15,102.55 MB | 1,326 MB | meds_tab | -## MIMIC-IV Dataset +### MIMIC-IV Dataset MEDS-Tab, `tsfresh`, and `catabra` were tested across three different patient scales on MIMIC-IV. From c6cc05995495ce73c0dd6227e96064dc57f5ac03 Mon Sep 17 00:00:00 2001 From: Oufattole Date: Tue, 2 Jul 2024 14:42:21 -0400 Subject: [PATCH 38/46] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e4d774c..1dd0be7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ classifiers = [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] -dependencies = ["polars", "pyarrow", "loguru", "hydra-core", "numpy", "scipy", "pandas", "tqdm", "xgboost", "scikit-learn", "hydra-optuna-sweeper", "hydra-joblib-launcher", "ml-mixins"] +dependencies = ["polars", "pyarrow", "loguru", "hydra-core", "numpy", "scipy<1.14.0", "pandas", "tqdm", "xgboost", "scikit-learn", "hydra-optuna-sweeper", "hydra-joblib-launcher", "ml-mixins"] [project.scripts] meds-tab-describe = "MEDS_tabular_automl.scripts.describe_codes:main" From d1ac6b2b78c95cb731c05d27cc2a9eab23013938 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 02:34:49 +0000 Subject: [PATCH 39/46] Cleaned up docs formating. Added versioning. Fixed typos. Added feature. Added logo --- docs/assets/dark_purple_meds_tab.png | Bin 0 -> 131218 bytes docs/assets/light_purple_meds_tab.png | Bin 0 -> 131494 bytes docs/assets/meds_logo.png | Bin 32672 -> 0 bytes docs/assets/white_meds_tab.png | Bin 0 -> 112663 bytes docs/generate.sh | 9 +++ docs/source/_static/switcher.json | 12 +++ docs/source/conf.py | 109 ++++++++++++++++++-------- docs/source/implementation.md | 19 +++-- docs/source/index.md | 8 +- docs/source/installation.md | 34 -------- docs/source/overview.md | 12 +-- src/MEDS_tabular_automl/__init__.py | 1 + 12 files changed, 119 insertions(+), 85 deletions(-) create mode 100644 docs/assets/dark_purple_meds_tab.png create mode 100644 docs/assets/light_purple_meds_tab.png delete mode 100644 docs/assets/meds_logo.png create mode 100644 docs/assets/white_meds_tab.png create mode 100644 docs/generate.sh create mode 100644 docs/source/_static/switcher.json delete mode 100644 docs/source/installation.md diff --git a/docs/assets/dark_purple_meds_tab.png 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Your contributions are welcome!", # noqa E501 + "show_version_warning_banner": True, + "switcher": { + "json_url": json_url, + "version_match": version_match, + }, + "navbar_center": ["version-switcher", "navbar-nav"], + "footer_start": ["copyright"], + "footer_center": ["sphinx-version"], + "use_edit_page_button": True, + # "secondary_sidebar_items": { + # "**/*": ["page-toc", "edit-this-page", "sourcelink"], + # }, + "back_to_top_button": True, +} -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. +html_context = { + "github_user": "mmcdermott", + "github_repo": "MEDS_Tabular_AutoML", + "github_version": "main", + "doc_path": "docs/source", +} -# Material theme options (see theme.conf for more information) # html_theme_options = { # # Set the name of the project to appear in the navigation. # "nav_title": "MEDS-TAB", @@ -263,9 +325,6 @@ def ensure_pandoc_installed(_): # } -html_static_path = ["_static"] - - html_show_copyright = True htmlhelp_basename = "meds-tab-doc" @@ -327,22 +386,10 @@ def ensure_pandoc_installed(_): # not chapters. # latex_use_parts = False -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - # -- Options for EPUB output epub_show_urls = "footnote" -print(f"loading configurations for {project} {version} ...", file=sys.stderr) +print(f"loading configurations for {project} {release} ...", file=sys.stderr) def setup(app): diff --git a/docs/source/implementation.md b/docs/source/implementation.md index 1bcd7c2..aabe6a2 100644 --- a/docs/source/implementation.md +++ b/docs/source/implementation.md @@ -47,9 +47,11 @@ The tabularization stage of our pipeline, exposed via the cli commands: Static data is relatively small in the medical datasets, so we use a dense pivot operation, convert it to a sparse matrix, and then duplicate rows such that the static data will match up with the time series data rows generated in the next step. Static data is currently processed serially. -The script for tabularizing time series data primarily transforms a raw, unstructured dataset into a structured, feature-rich dataset by utilizing a series of sophisticated data processing steps. This transformation involves converting raw time series from a Polars dataframe into a sparse matrix format, aggregating events that occur at the same date for the same patient, and then applying rolling window aggregations to extract temporal features. Here's a step-by-step breakdown of the algorithm: +The script for tabularizing time series data primarily transforms a raw, unstructured dataset into a structured, feature-rich dataset by utilizing a series of sophisticated data processing steps. This transformation (as depicted in the figure below) involves converting raw time series from a Polars dataframe into a sparse matrix format, aggregating events that occur at the same date for the same patient, and then applying rolling window aggregations to extract temporal features. -### High-Level Steps +![Time Series Tabularization Method](../assets/pivot.png) + +### High-Level Tabularization Algorithm 1. **Data Loading and Categorization**: @@ -57,17 +59,14 @@ The script for tabularizing time series data primarily transforms a raw, unstruc 2. **Sparse Matrix Conversion**: - - Data from the Polars dataframe is converted into a sparse matrix format. This step is crucial for efficient memory management, especially when dealing with large datasets. - -3. **Event Aggregation**: - - - Events that occur on the same date for the same patient are aggregated. This reduces redundancy in the data and significantly speeds up the rolling window aggregations on datasets that have lots of concurrent observations. + - Data from the Polars dataframe is converted into a sparse matrix format, where each row represents a unique event (patient x timestamp), and each column corresponds to a MEDS code for the patient. -4. **Rolling Window Aggregation**: +3. **Rolling Window Aggregation**: - - The aggregated data undergoes a rolling window operation where various statistical methods are applied (sum, count, min, max, etc.) to extract features over specified window sizes. + - For each aggregation method (sum, count, min, max, etc.), events that occur on the same date for the same patient are aggregated. This reduces the amount of data we have to perform rolling windows over. + - Then we aggregate features over the specified rolling windows sizes. -5. **Output Storage**: +4. **Output Storage**: - Sparse array is converted to Coordinate List format and stored as a `.npz` file on disk. - The file paths look as follows diff --git a/docs/source/index.md b/docs/source/index.md index 051ee46..8f75fb2 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -8,10 +8,10 @@ glob: maxdepth: 2 --- Overview -Pipeline/Architecture Details -Computational Profile -Prediction Performance -Module API Reference +Pipeline +Memory/CPU Usage +Performance +API ``` ______________________________________________________________________ diff --git a/docs/source/installation.md b/docs/source/installation.md deleted file mode 100644 index b2bfcf4..0000000 --- a/docs/source/installation.md +++ /dev/null @@ -1,34 +0,0 @@ -# Scalable tabularization and tabular feature usage utilities over generic MEDS datasets - -

- Python - PyPI - Hydra - Codecov - Tests - Code Quality - Documentation Status - Contributors - Pull Requests - License -

- -This repository provides utilities and scripts to run limited automatic tabular ML pipelines for generic MEDS -datasets. - -# Installation - -To use MEDS-Tab, install the dependencies following commands below: - -**Pip Install** - -```console -pip install meds-tab -``` - -**Local Install** - -```console -# clone the git repo -pip install . -``` diff --git a/docs/source/overview.md b/docs/source/overview.md index 9d5ad8e..5132c11 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -42,14 +42,14 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au 2. **`meds-tab-tabularize-static`**: Filters and processes the dataset based on the frequency of codes, generating a tabular vector for each patient at each timestamp in the shards. Each row corresponds to a unique `patient_id` and `timestamp` combination, thus rows are duplicated across multiple timestamps for the same patient. - **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` + **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, static/first, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` ```console meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=[1d,30d,365d,full] \ do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" + tabularization.aggs=[static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" ``` - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl/utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) @@ -66,12 +66,12 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au tabularization.min_code_inclusion_frequency=10 \ do_overwrite=False \ tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.aggs=[static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. - **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) + **Example: Align tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) ```console meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ @@ -79,7 +79,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au tabularization.min_code_inclusion_frequency=10 \ do_overwrite=False \ tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.aggs=[static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). @@ -92,7 +92,7 @@ See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_Au tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ do_overwrite=False \ - tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + tabularization.aggs=$(generate-permutations [static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` ## Additional CLI Scripts diff --git a/src/MEDS_tabular_automl/__init__.py b/src/MEDS_tabular_automl/__init__.py index e69de29..3b93d0b 100644 --- a/src/MEDS_tabular_automl/__init__.py +++ b/src/MEDS_tabular_automl/__init__.py @@ -0,0 +1 @@ +__version__ = "0.0.2" From c82703b7d06e3a6c39594384205d84115ba82eb4 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 03:00:35 +0000 Subject: [PATCH 40/46] updated versions --- docs/source/_static/switcher.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/_static/switcher.json b/docs/source/_static/switcher.json index 3f73aed..226637e 100644 --- a/docs/source/_static/switcher.json +++ b/docs/source/_static/switcher.json @@ -1,12 +1,12 @@ [ { "version": "dev", - "url": "https://pydata-sphinx-theme.readthedocs.io/en/latest/" + "url": "https://meds-tab.readthedocs.io/en/latest/" }, { "name": "0.0.2 (stable)", "version": "v0.0.2", - "url": "https://pydata-sphinx-theme.readthedocs.io/en/stable/", + "url": "https://meds-tab.readthedocs.io/en/stable/", "preferred": true }, ] From 1da8e6da69388927208149e4933b09eeaced8a33 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 03:23:40 +0000 Subject: [PATCH 41/46] updated the switcher json file which had a bug --- docs/source/_static/switcher.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/_static/switcher.json b/docs/source/_static/switcher.json index 226637e..ba475ce 100644 --- a/docs/source/_static/switcher.json +++ b/docs/source/_static/switcher.json @@ -8,5 +8,5 @@ "version": "v0.0.2", "url": "https://meds-tab.readthedocs.io/en/stable/", "preferred": true - }, + } ] From 40e85ce00f2c13ab8f108559b7a9bbc0bf65985e Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 03:52:17 +0000 Subject: [PATCH 42/46] updated dev version switcher for config so dev version is associated with the latest readthedocs page --- docs/source/conf.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 7c370f9..e08b1d7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,14 +19,14 @@ # Define the json_url for our version switcher. -json_url = "https://meds-tab.readthedocs.io/en/latest/_static/switcher.json" +json_url = "https://meds-tab.readthedocs.io/en/dev/_static/switcher.json" # Define the version we use for matching in the version switcher. version_match = os.environ.get("READTHEDOCS_VERSION") release = MEDS_tabular_automl.__version__ # If READTHEDOCS_VERSION doesn't exist, we're not on RTD # If it is an integer, we're in a PR build and the version isn't correct. # If it's "latest" → change to "dev" (that's what we want the switcher to call it) -if not version_match or version_match.isdigit() or version_match == "latest": +if not version_match or version_match.isdigit(): # For local development, infer the version to match from the package. if "dev" in release or "rc" in release: version_match = "dev" @@ -35,6 +35,8 @@ json_url = "_static/switcher.json" else: version_match = f"v{release}" +elif version_match == "latest": + version_match = "dev" elif version_match == "stable": version_match = f"v{release}" From 0d148d71bf4f642d5f582928531fbba0b1f5578a Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 04:00:02 +0000 Subject: [PATCH 43/46] updated switcher json to point to the latest read the docs page --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index e08b1d7..bac8f33 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,7 +19,7 @@ # Define the json_url for our version switcher. -json_url = "https://meds-tab.readthedocs.io/en/dev/_static/switcher.json" +json_url = "https://meds-tab.readthedocs.io/en/latest/_static/switcher.json" # Define the version we use for matching in the version switcher. version_match = os.environ.get("READTHEDOCS_VERSION") release = MEDS_tabular_automl.__version__ From 11e462333a2dca36113bbb17e5f5261a11cead05 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 04:25:38 +0000 Subject: [PATCH 44/46] cleaned up tables --- docs/source/prediction.md | 156 +++++++++++++++++++------------------- docs/source/profiling.md | 38 +++++----- 2 files changed, 97 insertions(+), 97 deletions(-) diff --git a/docs/source/prediction.md b/docs/source/prediction.md index 2f52452..35131d8 100644 --- a/docs/source/prediction.md +++ b/docs/source/prediction.md @@ -46,19 +46,19 @@ Since this includes every window size and aggregation, it is the most expensive #### 1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks -| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | -| ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | -| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | -| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | -| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | -| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | -| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | -| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | -| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | -| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | -| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | -| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | -| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | +| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | +| ------------------------------- | ----------------- | --------- | --------- | -------- | ---------------- | ----------------- | +| Post-discharge 30 day Mortality | Discharge | 2m59s | 3m38s | 0m38s | 9,037 | 11,955 | +| Post-discharge 1 year Mortality | Discharge | 5m16s | 6m10s | 0m59s | 10,804 | 12,330 | +| 30 day Readmission | Discharge | 2m30s | 3m3s | 0m39s | 13,199 | 18,677 | +| In ICU Mortality | Admission + 24 hr | 0m38s | 1m3s | 0m13s | 1,712 | 2,986 | +| In ICU Mortality | Admission + 48 hr | 0m34s | 1m1s | 0m13s | 1,613 | 2,770 | +| In Hospital Mortality | Admission + 24 hr | 2m8s | 2m41s | 0m32s | 9,072 | 12,056 | +| In Hospital Mortality | Admission + 48 hr | 1m54s | 2m25s | 0m29s | 8,858 | 12,371 | +| LOS in ICU > 3 days | Admission + 24 hr | 2m3s | 2m37s | 0m28s | 4,650 | 5,715 | +| LOS in ICU > 3 days | Admission + 48 hr | 1m44s | 2m18s | 0m24s | 4,453 | 5,577 | +| LOS in Hospital > 3 days | Admission + 24 hr | 6m5s | 7m5s | 1m4s | 11,012 | 12,223 | +| LOS in Hospital > 3 days | Admission + 48 hr | 6m10s | 7m12s | 1m4s | 10,703 | 11,830 | #### 1.2 MIMIC-IV Task Specific Training Cohort Size @@ -66,17 +66,17 @@ To better understand the runtimes, we also report the task specific cohort size. | Task | Index Timestamp | Number of Patients | Number of Events | | ------------------------------- | ----------------- | ------------------ | ---------------- | -| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | -| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | -| 30 day Readmission | Discharge | 17418 | 377785 | -| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | -| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | -| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | -| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | -| LOS in ICU > 3 days | Admission + 24 hr | 42809 | 61342 | -| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | -| LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | -| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | +| Post-discharge 30 day Mortality | Discharge | 149,014 | 356,398 | +| Post-discharge 1 year Mortality | Discharge | 149,014 | 356,398 | +| 30 day Readmission | Discharge | 17,418 | 377,785 | +| In ICU Mortality | Admission + 24 hr | 7,839 | 22,811 | +| In ICU Mortality | Admission + 48 hr | 6,750 | 20,802 | +| In Hospital Mortality | Admission + 24 hr | 51,340 | 338,614 | +| In Hospital Mortality | Admission + 48 hr | 47,231 | 348,289 | +| LOS in ICU > 3 days | Admission + 24 hr | 42,809 | 61,342 | +| LOS in ICU > 3 days | Admission + 48 hr | 42,805 | 61,327 | +| LOS in Hospital > 3 days | Admission + 24 hr | 152,126 | 360,208 | +| LOS in Hospital > 3 days | Admission + 48 hr | 152,120 | 359,020 | ### 2. MIMIC-IV Sweep @@ -124,19 +124,19 @@ For a complete example on MIMIC-IV and for all of our config files, see the [MIM #### 2.1 XGBoost Performance on MIMIC-IV -| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | -| ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | -| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | -| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | -| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | -| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | \[1d,full\] | \[static/present,code/count,value/count\] | -| LOS in ICU > 3 days | Admission + 24 hr | 0.9455099633 | 30443 | 1624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | -| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ----- | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.935 | 1,371 | 5,712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.898 | 289 | 10,048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | +| 30 day Readmission | Discharge | 0.708 | 303 | 9,903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| In ICU Mortality | Admission + 24 hr | 0.661 | 7,059 | 3,037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In ICU Mortality | Admission + 48 hr | 0.673 | 71 | 16,112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.812 | 43 | 18,989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.810 | 678 | 7,433 | \[1d,full\] | \[static/present,code/count,value/count\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.946 | 30,443 | 1,624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.967 | 2,864 | 4,332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.943 | 94,633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.945 | 30,880 | 1,619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | - Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. @@ -144,19 +144,19 @@ For a complete example on MIMIC-IV and for all of our config files, see the [MIM Additionally, the model parameters from the highest performing run are reported below. -| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | -| ------------------------------- | ----------------- | -------------- | -------------- | ------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | -| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | -| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | -| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | -| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | -| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | -| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | -| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | -| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | -| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | ----- | ------ | ----- | --------- | -------------------- | ------------------------- | --------------------- | -------------- | +| Post-discharge 30 day Mortality | Discharge | 0.006 | 0.032 | 0.374 | 0.572 | 53 | 703 | 9 | 16 | +| Post-discharge 1 year Mortality | Discharge | 0.009 | 0.086 | 0.343 | 0.899 | 76 | 858 | 9 | 11 | +| 30 day Readmission | Discharge | 0.006 | 0.359 | 0.374 | 0.673 | 53 | 712 | 9 | 16 | +| In ICU Mortality | Admission + 24 hr | 0.038 | 0.062 | 0.231 | 0.995 | 89 | 513 | 7 | 14 | +| In ICU Mortality (first 48h) | Admission + 48 hr | 0.044 | 0.041 | 0.289 | 0.961 | 91 | 484 | 5 | 14 | +| In Hospital Mortality | Admission + 24 hr | 0.028 | 0.013 | 0.011 | 0.567 | 11 | 454 | 6 | 9 | +| In Hospital Mortality | Admission + 48 hr | 0.011 | 0.060 | 0.179 | 0.964 | 84 | 631 | 7 | 13 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.012 | 0.090 | 0.137 | 0.626 | 26 | 650 | 8 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.012 | 0.049 | 0.200 | 0.960 | 84 | 615 | 7 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.008 | 0.067 | 0.255 | 0.989 | 90 | 526 | 5 | 14 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.001 | 0.030 | 0.028 | 0.967 | 9 | 538 | 8 | 7 | ## XGBoost Model Performance on eICU Tasks @@ -168,39 +168,39 @@ For more details about eICU specific task generation and running, see the [eICU #### 1. XGBoost Performance on eICU -| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | -| ------------------------------- | ----------------- | ------------ | -------------------------------- | ------------------------ | -------------------------------------------------------------- | -| Post-discharge 30 day Mortality | Discharge | 0.6027428797 | 68235 | \[12h,1d,full\] | \[code/count,value/sum_sqd,value/max\] | -| Post-discharge 1 year Mortality | Discharge | 0.8745313425 | 3280 | \[30d,365d\] | \[static/present,value/sum,value/sum_sqd,value/min,value/max\] | -| In Hospital Mortality | Admission + 24 hr | 0.8552631579 | 335912 | \[2h,7d,30d,365d,full\] | \[static/present,code/count,value/count,value/min,value/max\] | -| In Hospital Mortality | Admission + 48 hr | 0.5703997648 | 89121 | \[12h,1d,30d\] | \[code/count,value/count,value/min\] | -| LOS in ICU > 3 days | Admission + 24 hr | 0.7834375547 | 7881 | \[1d,30d,full\] | \[static/present,code/count,value/count,value/sum,value/max\] | -| LOS in ICU > 3 days | Admission + 48 hr | 0.7574691571 | 1719 | \[2h,12h,7d,30d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/min\] | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.8640973238 | 160 | \[1d,30d,365d,full\] | \[static/present,code/count,value/min,value/max\] | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.8950621974 | 975 | \[12h,1d,30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd\] | +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ----- | -------------------------------- | ------------------------ | -------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.603 | 68,235 | \[12h,1d,full\] | \[code/count,value/sum_sqd,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.875 | 3,280 | \[30d,365d\] | \[static/present,value/sum,value/sum_sqd,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.855 | 335,912 | \[2h,7d,30d,365d,full\] | \[static/present,code/count,value/count,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.570 | 89,121 | \[12h,1d,30d\] | \[code/count,value/count,value/min\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.783 | 7,881 | \[1d,30d,full\] | \[static/present,code/count,value/count,value/sum,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.757 | 1,719 | \[2h,12h,7d,30d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/min\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.864 | 160 | \[1d,30d,365d,full\] | \[static/present,code/count,value/min,value/max\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.895 | 975 | \[12h,1d,30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd\] | #### 2. XGBoost Optimal Found Model Parameters -| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | -| ------------------------------- | ----------------- | -------------- | -------------- | -------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | -| In Hospital Mortality | Admission + 24 hr | 0.04328401484 | 0.001377527826 | 0.3425861261 | 0.8788657019 | 13.25388316 | 574 | 9 | 14 | -| In Hospital Mortality | Admission + 48 hr | 0.00156138886 | 0.001669486342 | 0.3026235563 | 0.7250077856 | 0.05549772671 | 939 | 9 | 12 | -| LOS in ICU > 3 days | Admission + 24 hr | 0.2103487211 | 0.1889039915 | 0.05310740103 | 0.9549160412 | 4.693180409 | 359 | 6 | 14 | -| LOS in ICU > 3 days | Admission + 48 hr | 0.3399653331 | 0.3930942838 | 0.003662725623 | 0.9002656342 | 6.398461636 | 394 | 10 | 13 | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.02626332529 | 0.2375031032 | 0.03299434213 | 0.9401763566 | 46.19807109 | 909 | 5 | 11 | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.100031978 | 0.590017142 | 0.01514768214 | 0.9135673756 | 57.99367271 | 499 | 10 | 9 | -| Post-discharge 30 day Mortality | Discharge | 0.003120329768 | 0.01157485817 | 0.001488659801 | 0.7295192432 | 12.6755339 | 986 | 7 | 7 | -| Post-discharge 1 year Mortality | Discharge | 0.004953313969 | 0.005881577613 | 0.001786936591 | 0.6897205622 | 93.18348266 | 938 | 6 | 14 | +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | ----- | ------ | ----- | --------- | -------------------- | ------------------------- | --------------------- | -------------- | +| In Hospital Mortality | Admission + 24 hr | 0.043 | 0.001 | 0.343 | 0.879 | 13 | 574 | 9 | 14 | +| In Hospital Mortality | Admission + 48 hr | 0.002 | 0.002 | 0.303 | 0.725 | 0 | 939 | 9 | 12 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.210 | 0.189 | 0.053 | 0.955 | 5 | 359 | 6 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.340 | 0.393 | 0.004 | 0.900 | 6 | 394 | 10 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.026 | 0.238 | 0.033 | 0.940 | 46 | 909 | 5 | 11 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.100 | 0.590 | 0.015 | 0.914 | 58 | 499 | 10 | 9 | +| Post-discharge 30 day Mortality | Discharge | 0.003 | 0.0116 | 0.001 | 0.730 | 13 | 986 | 7 | 7 | +| Post-discharge 1 year Mortality | Discharge | 0.005 | 0.006 | 0.002 | 0.690 | 93 | 938 | 6 | 14 | #### 3. eICU Task Specific Training Cohort Size | Task | Index Timestamp | Number of Patients | Number of Events | | ------------------------------- | ----------------- | ------------------ | ---------------- | -| Post-discharge 30 day Mortality | Discharge | 91405 | 91405 | -| Post-discharge 1 year Mortality | Discharge | 91405 | 91405 | -| In Hospital Mortality | Admission + 24 hr | 3585 | 3585 | -| In Hospital Mortality | Admission + 48 hr | 1527 | 1527 | -| LOS in ICU > 3 days | Admission + 24 hr | 12672 | 14004 | -| LOS in ICU > 3 days | Admission + 48 hr | 12712 | 14064 | -| LOS in Hospital > 3 days | Admission + 24 hr | 99540 | 99540 | -| LOS in Hospital > 3 days | Admission + 48 hr | 99786 | 99786 | +| Post-discharge 30 day Mortality | Discharge | 91,405 | 91,405 | +| Post-discharge 1 year Mortality | Discharge | 91,405 | 91,405 | +| In Hospital Mortality | Admission + 24 hr | 35,85 | 3,585 | +| In Hospital Mortality | Admission + 48 hr | 1,527 | 1,527 | +| LOS in ICU > 3 days | Admission + 24 hr | 12,672 | 14,004 | +| LOS in ICU > 3 days | Admission + 48 hr | 12,712 | 14,064 | +| LOS in Hospital > 3 days | Admission + 24 hr | 99,540 | 99,540 | +| LOS in Hospital > 3 days | Admission + 48 hr | 99,786 | 99,786 | diff --git a/docs/source/profiling.md b/docs/source/profiling.md index 3ed144e..5a0c083 100644 --- a/docs/source/profiling.md +++ b/docs/source/profiling.md @@ -31,17 +31,17 @@ a) 100 Patients **Table 1: 6,374 Codes, 2,065,608 Rows, Output Shape \[132,461, 6,374\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ----------- | ------------ | ----------- | -------- | -| 0m39.426s | 5,271.85 MB | 14,791.83 MB | 362 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m39.426s | 5,271 MB | 14,791 MB | 362 MB | meds_tab | b) 500 Patients **Table 2: 18,314 Codes, 8,737,355 Rows, Output Shape \[565,014, 18,314\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ----------- | ------------ | ----------- | -------- | -| 3m4.435s | 8,335.44 MB | 15,102.55 MB | 1,326 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 3m4.435s | 8,335 MB | 15,102 MB | 1,326 MB | meds_tab | ### MIMIC-IV Dataset @@ -53,11 +53,11 @@ This table illustrates the efficiency of MEDS-Tab in processing a small subset o **Table 3: 1,504 Codes, 23,346 Rows, Output Shape \[2,127, 1,504\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ------------ | ------------- | ----------- | -------- | -| 0m2.071s | 423.78 MB | 943.984 MB | 7 MB | meds_tab | -| 1m41.920s | 84,159.44 MB | 265,877.86 MB | 1 MB | tsfresh | -| 0m15.366s | 2,537.46 MB | 4,781.824 MB | 1 MB | catabra | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m2.071s | 423 MB | 943 MB | 7 MB | meds_tab | +| 1m41.920s | 84,159 MB | 265,877 MB | 1 MB | tsfresh | +| 0m15.366s | 2,537 MB | 4,781 MB | 1 MB | catabra | b) 100 Patients @@ -65,11 +65,11 @@ The performance gap was further highlighted with an increased number of patients **Table 4: 4,154 Codes, 150,789 Rows, Output Shape \[15,664, 4,154\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ------------- | ------------- | ----------- | -------- | -| 0m4.724s | 718.76 MB | 1,167.29 MB | 45 MB | meds_tab | -| 5m9.077s | 217,477.52 MB | 659,735.25 MB | 4 MB | tsfresh | -| 3m17.671s | 14,319.53 MB | 28,342.81 MB | 4 MB | catabra | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m4.724s | 718 MB | 1,167 MB | 45 MB | meds_tab | +| 5m9.077s | 217,477 MB | 659,735 MB | 4 MB | tsfresh | +| 3m17.671s | 14,319 MB | 28,342 MB | 4 MB | catabra | c) 500 Patients @@ -77,8 +77,8 @@ Scaling further to 500 patients, MEDS-Tab maintained consistent performance, rei **Table 5: 48,115 Codes, 795,368 Rows, Output Shape \[75,595, 8,115\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ----------- | ----------- | ----------- | -------- | -| 0m15.867s | 1,410.79 MB | 3,539.32 MB | 442 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m15.867s | 1,410 MB | 3,539 MB | 442 MB | meds_tab | ______________________________________________________________________ From a7cb194e6d428b12f34292e73fe9939abbcf8a0a Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 04:30:26 +0000 Subject: [PATCH 45/46] updated the readme --- README.md | 363 ++++++++++++++++++++++++++++++------------------------ 1 file changed, 202 insertions(+), 161 deletions(-) diff --git a/README.md b/README.md index 911d4ce..c3b7fec 100644 --- a/README.md +++ b/README.md @@ -16,28 +16,11 @@ This repository provides utilities and scripts to run limited automatic tabular ML pipelines for generic MEDS datasets. -# Installation - -To use MEDS-Tab, install the dependencies following commands below: - -**Pip Install** - -```console -pip install meds-tab -``` - -**Local Install** - -```console -# clone the git repo -pip install . -``` - # Usage This repository consists of two key pieces: -1. Construction of and efficient loading of tabular (flat, non-longitudinal) summary features describing patient records in MEDS over arbitrary time-windows (e.g. 1 year, 6 months, etc.) backwards in time from a given index date. +1. Construction and efficient loading of tabular (flat, non-longitudinal) summary features describing patient records in MEDS over arbitrary time windows (e.g. 1 year, 6 months, etc.), which go backwards in time from a given index date. 2. Running a basic XGBoost AutoML pipeline over these tabular features to predict arbitrary binary classification or regression downstream tasks defined over these datasets. The "AutoML" part of this is not particularly advanced -- what is more advanced is the efficient construction, storage, and loading of tabular features for the candidate AutoML models, enabling a far more extensive search over a much larger total number of features than prior systems. ## Quick Start @@ -62,7 +45,29 @@ pip install . For an end to end example over MIMIC-IV, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). For an end to end example over Philips eICU, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). -See `tests/test_integration.py` for a local example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. +See [`/tests/test_integration.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/tests/test_integration.py) for a local example of the end-to-end pipeline being run on synthetic data. This script is a functional test that is also run with `pytest` to verify the correctness of the algorithm. + +## Why MEDS-Tab? + +MEDS-Tab is a comprehensive framework designed to streamline the handling, modeling, and analysis of complex medical time-series data. By leveraging automated processes, MEDS-Tab significantly reduces the computation required to generate high-quality baseline models for diverse supervised learning tasks. + +- Cost Efficiency: MEDS-Tab is dramatically more cost-effective compared to existing solutions +- Strong Performance: MEDS-Tab provides robustness and high performance across various datasets compared with other frameworks. + +### I. Transform to MEDS + +MEDS-Tab leverages the recently developed, minimal, easy-to-use Medical Event Data Standard (MEDS) schema to standardize structured EHR data to a consistent schema from which baselines can be reliably produced across arbitrary tasks and settings. In order to use MEDS-Tab, you will first need to transform your raw EHR data to a MEDS format, which can be done using the following libraries: + +- [MEDS Polars](https://github.com/mmcdermott/MEDS_polars_functions) for a set of functions and scripts for extraction to and transformation/pre-processing of MEDS-formatted data. +- [MEDS ETL](https://github.com/Medical-Event-Data-Standard/meds_etl) for a collection of ETLs from common data formats to MEDS. The package library currently supports MIMIC-IV, OMOP v5, and MEDS FLAT (a flat version of MEDS). + +### II. Run MEDS-Tab + +- Run the MEDS-Tab Command-Line Interface tool (`MEDS-Tab-cli`) to extract cohorts based on your task - check out the [Usage Guide](https://meds-tab--36.org.readthedocs.build/en/36/overview.html#core-cli-scripts-overview)! + +- Painless Reproducibility: Use [MEDS-Tab](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV/tree/main/tasks) to obtain comparable, reproducible, and well-tuned XGBoost results tailored to your dataset-specific feature space! + +By following these steps, you can seamlessly transform your dataset, define necessary criteria, and leverage powerful machine learning tools within the MEDS-Tab ecosystem. This approach not only simplifies the process but also ensures high-quality, reproducible results for your machine learning tasks for health projects. It can reliably take no more than a week of full-time human effort to perform Steps I-V on new datasets in reasonable raw formulations! ## Core CLI Scripts Overview @@ -77,17 +82,17 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b 2. **`meds-tab-tabularize-static`**: Filters and processes the dataset based on the frequency of codes, generating a tabular vector for each patient at each timestamp in the shards. Each row corresponds to a unique `patient_id` and `timestamp` combination, thus rows are duplicated across multiple timestamps for the same patient. - **Example: Tabularizing static data** with the minimum code frequency of 10 and window sizes of `[1d, 30d, 365d, full]` and value aggregation methods of `[static/present, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` + **Example: Tabularizing static data** with the minimum code frequency of 10, window sizes of `[1d, 30d, 365d, full]`, and value aggregation methods of `[static/present, static/first, code/count, value/count, value/sum, value/sum_sqd, value/min, value/max]` ```console meds-tab-tabularize-static MEDS_cohort_dir="path_to_data" \ - tabularization.min_code_inclusion_frequency=10 \ - tabularization.window_sizes=[1d,30d,365d,full] \ - do_overwrite=False \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.min_code_inclusion_frequency=10 \ + tabularization.window_sizes=[1d,30d,365d,full] \ + do_overwrite=False \ + tabularization.aggs=[static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]" ``` - - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl /utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) + - For the exhuastive examples of value aggregations, see [`/src/MEDS_tabular_automl/utils.py`](https://github.com/mmcdermott/MEDS_Tabular_AutoML/blob/main/src/MEDS_tabular_automl/utils.py#L24) 3. **`meds-tab-tabularize-time-series`**: Iterates through combinations of a shard, `window_size`, and `aggregation` to generate feature vectors that aggregate patient data for each unique `patient_id` x `timestamp`. This stage (and the previous stage) use sparse matrix formats to efficiently handle the computational and storage demands of rolling window calculations on large datasets. We support parallelization through Hydra's [`--multirun`](https://hydra.cc/docs/intro/#multirun) flag and the [`joblib` launcher](https://hydra.cc/docs/plugins/joblib_launcher/#internaldocs-banner). @@ -101,12 +106,12 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b tabularization.min_code_inclusion_frequency=10 \ do_overwrite=False \ tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.aggs=[static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 4. **`meds-tab-cache-task`**: Aligns task-specific labels with the nearest prior event in the tabularized data. It requires a labeled dataset directory with three columns (`patient_id`, `timestamp`, `label`) structured similarly to the `MEDS_cohort_dir`. - **Example: Aligh tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) + **Example: Align tabularized data** for a specific task `$TASK` and labels that has pulled from [ACES](https://github.com/justin13601/ACES) ```console meds-tab-cache-task MEDS_cohort_dir="path_to_data" \ @@ -114,7 +119,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b tabularization.min_code_inclusion_frequency=10 \ do_overwrite=False \ tabularization.window_sizes=[1d,30d,365d,full] \ - tabularization.aggs=[static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] + tabularization.aggs=[static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max] ``` 5. **`meds-tab-xgboost`**: Trains an XGBoost model using user-specified parameters. Permutations of `window_sizes` and `aggs` can be generated using `generate-permutations` command (See the section below for descriptions). @@ -127,7 +132,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b tabularization.min_code_inclusion_frequency=10 \ tabularization.window_sizes=$(generate-permutations [1d,30d,365d,full]) \ do_overwrite=False \ - tabularization.aggs=$(generate-permutations [static/present,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) + tabularization.aggs=$(generate-permutations [static/present,static/first,code/count,value/count,value/sum,value/sum_sqd,value/min,value/max]) ``` ## Additional CLI Scripts @@ -137,7 +142,7 @@ See `tests/test_integration.py` for a local example of the end-to-end pipeline b For example you can directly call **`generate-permutations`** in the command line: ```console - generate-permutations [2,3,4] + generate-permutations [2,3,4] \ [2], [2, 3], [2, 3, 4], [2, 4], [3], [3, 4], [4] ``` @@ -182,8 +187,6 @@ MEDS-Tab has several key limitations which we plan to address in future changes. 3. We need to ensure full and seamless compatibility with the ACES CLI tool, rather than relying on the python API and manual adjustments: [#34](https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues/34) -# How does MEDS-Tab Work? - ## What do you mean "tabular pipelines"? Isn't _all_ structured EHR data already tabular? This is a common misconception. _Tabular_ data refers to data that can be organized in a consistent, logical @@ -197,30 +200,30 @@ realize these constraints, which will explicitly involve summarizing the patient or future windows in time to produce a single row per patient with a consistent, logical set of columns (though there may still be missingness). -## The MEDS-Tab Architecture +# The MEDS-Tab Architecture -In this section we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. +In this section, we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. -We break our method into 4 discrete parts +We break our method into 4 discrete parts: 1. Describe codes (compute feature frequencies) -2. Given time series data tabularize it -3. cache task specific rows of data for efficient loading +2. Tabularization of time-series data +3. Efficient data caching for task-specific rows 4. XGBoost training -### 1. Describe Codes (Compute Feature Frequencies) +## 1. Describe Codes (compute feature frequencies) This initial stage processes a pre-shareded dataset. We expect a structure as follows where each shard contains a subset of the patients: -``` +```text /PATH/TO/MEDS/DATA │ -└─── +└─── │ │ .parquet │ │ .parquet │ │ ... │ -└─── +└─── │ │ .parquet │ │ .parquet | │ ... @@ -235,9 +238,7 @@ We then compute and store feature frequencies, crucial for determining which fea - **Data Loading and Sharding**: We iterate through shards to compute feature frequencies for each shard. - **Frequency Aggregation**: After computing frequencies across shards, we aggregate them to get a final count of each feature across the entire dataset training dataset, which allows us to filter out infrequent features in the tabularization stage or when tuning XGBoost. -This outputs parquet file \`\` - -### 2. Tabularization of Time Series Data +## 2. Tabularization of Time-Series Data ### Overview @@ -248,9 +249,11 @@ The tabularization stage of our pipeline, exposed via the cli commands: Static data is relatively small in the medical datasets, so we use a dense pivot operation, convert it to a sparse matrix, and then duplicate rows such that the static data will match up with the time series data rows generated in the next step. Static data is currently processed serially. -The script for tabularizing time series data primarily transforms a raw, unstructured dataset into a structured, feature-rich dataset by utilizing a series of sophisticated data processing steps. This transformation involves converting raw time series from a Polars dataframe into a sparse matrix format, aggregating events that occur at the same date for the same patient, and then applying rolling window aggregations to extract temporal features. Here's a step-by-step breakdown of the algorithm: +The script for tabularizing time series data primarily transforms a raw, unstructured dataset into a structured, feature-rich dataset by utilizing a series of sophisticated data processing steps. This transformation (as depicted in the figure below) involves converting raw time series from a Polars dataframe into a sparse matrix format, aggregating events that occur at the same date for the same patient, and then applying rolling window aggregations to extract temporal features. + +![Time Series Tabularization Method](../assets/pivot.png) -### High-Level Steps +### High-Level Tabularization Algorithm 1. **Data Loading and Categorization**: @@ -258,26 +261,23 @@ The script for tabularizing time series data primarily transforms a raw, unstruc 2. **Sparse Matrix Conversion**: - - Data from the Polars dataframe is converted into a sparse matrix format. This step is crucial for efficient memory management, especially when dealing with large datasets. - -3. **Event Aggregation**: + - Data from the Polars dataframe is converted into a sparse matrix format, where each row represents a unique event (patient x timestamp), and each column corresponds to a MEDS code for the patient. - - Events that occur on the same date for the same patient are aggregated. This reduces redundancy in the data and significantly speeds up the rolling window aggregations on datasets that have lots of concurrent observations. +3. **Rolling Window Aggregation**: -4. **Rolling Window Aggregation**: + - For each aggregation method (sum, count, min, max, etc.), events that occur on the same date for the same patient are aggregated. This reduces the amount of data we have to perform rolling windows over. + - Then we aggregate features over the specified rolling windows sizes. - - The aggregated data undergoes a rolling window operation where various statistical methods are applied (sum, count, min, max, etc.) to extract features over specified window sizes. - -5. **Output Storage**: +4. **Output Storage**: - Sparse array is converted to Coordinate List format and stored as a `.npz` file on disk. - The file paths look as follows -``` +```text /PATH/TO/MEDS/TABULAR_DATA │ -└─── - ├─── +└─── + ├─── │ ├───code │ │ └───count.npz │ └───value @@ -285,7 +285,7 @@ The script for tabularizing time series data primarily transforms a raw, unstruc ... ``` -### 3. Efficient Data Caching for Task-Specific Rows +## 3. Efficient Data Caching for Task-Specific Rows Now that we have generated tabular features for all the events in our dataset, we can cache subsets relevant for each task we wish to train a supervised model on. This step is critical for efficiently training machine learning models on task-specific data without having to load the entire dataset. @@ -294,9 +294,9 @@ Now that we have generated tabular features for all the events in our dataset, w - **Row Selection Based on Tasks**: Only the data rows that are relevant to the specific tasks are selected and cached. This reduces the memory footprint and speeds up the training process. - **Use of Sparse Matrices for Efficient Storage**: Sparse matrices are again employed here to store the selected data efficiently, ensuring that only non-zero data points are kept in memory, thus optimizing both storage and retrieval times. -The file structure for the cached data mirrors the tabular data and alsi is `.npz` files, and users must specify the directory to labels that follow the same shard filestructure as the input meds data from step (1). Label parquets need `patient_id`, `timestamp`, and `label` columns. +The file structure for the cached data mirrors that of the tabular data, also consisting of `.npz` files, where users must specify the directory that stores labels. Labels follow the same shard filestructure as the input meds data from step (1), and the label parquets need `patient_id`, `timestamp`, and `label` columns. -### 4. XGBoost Training +## 4. XGBoost Training The final stage uses the processed and cached data to train an XGBoost model. This stage is optimized to handle the sparse data structures produced in earlier stages efficiently. @@ -304,14 +304,12 @@ The final stage uses the processed and cached data to train an XGBoost model. Th - **Iterator for Data Loading**: Custom iterators are designed to load sparse matrices efficiently into the XGBoost training process, which can handle sparse inputs natively, thus maintaining high computational efficiency. - **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance. -- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimu code inclusion frequency. +- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window sizes, and the minimimum code inclusion frequency. # Computational Performance vs. Existing Pipelines Evaluating the computational overhead of tabularization methods is essential for assessing their efficiency and suitability for large-scale medical data processing. This section presents a comparative analysis of the computational overhead of MEDS-Tab with other systems like Catabra and TSFresh. It outlines the performance of each system in terms of wall time, memory usage, and output size, highlighting the computational efficiency and scalability of MEDS-Tab. -______________________________________________________________________ - ## 1. System Comparison Overview The systems compared in this study represent different approaches to data tabularization, with the main difference being MEDS-Tab usage of sparse tabularization. Specifically, for comparison we used: @@ -322,20 +320,18 @@ The systems compared in this study represent different approaches to data tabula The benchmarking tests were conducted using the following hardware and software settings: - **CPU Specification**: 2 x AMD EPYC 7713 64-Core Processor -- \*\*RAM Specification: 1024GB, 3200MHz, DDR4 +- **RAM Specification**: 1024GB, 3200MHz, DDR4 - **Software Environment**: Ubuntu 22.04.4 LTS -## MEDS-Tab Tabularization Technique +### MEDS-Tab Tabularization Technique -Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. We find that on the MIMICIV and EICU medical datasets we significantly outperform past methods. `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for EICU, while our method can scale to process hundreds of patients with low memory usage. We present the results below. +Tabularization of time-series data, as depecited above, is commonly used in several past works. The only two libraries to our knowledge that provide a full tabularization pipeline are `tsfresh` and `catabra`. `catabra` also offers a slower but more memory efficient version of their method which we denote `catabra-mem`. Other libraries either provide only rolling window functionalities (`featuretools`) or just pivoting operations (`Temporai`/`Clairvoyance`, `sktime`, `AutoTS`). We provide a significantly faster and more memory efficient method. Our findings show that on the MIMIC-IV and eICU medical datasets we significantly outperform both above-mentioned methods that provide similar functionalities with MEDS-Tab. While `catabra` and `tsfresh` could not even run within a budget of 10 minutes on as low as 10 patient's data for eICU, our method scales to process hundreds of patients with low memory usage under the same time budget. We present the results below. ## 2. Comparative Performance Analysis -The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. We additionally use a budget of 10 minutes as these are very small number of patients (10, 100, and 500 patients), and should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. - -## eICU Dataset +The tables below detail computational resource utilization across two datasets and various patient scales, emphasizing the better performance of MEDS-Tab in all of the scenarios. The tables are organized by dataset and number of patients. For the analysis, the full window sizes and the aggregation method code_count were used. Additionally, we use a budget of 10 minutes for running our tests given that for such small number of patients (10, 100, and 500 patients) data should be processed quickly. Note that `catabra-mem` is omitted from the tables as it never completed within the 10 minute budget. -______________________________________________________________________ +### eICU Dataset The only method that was able to tabularize eICU data was MEDS-Tab. We ran our method with both 100 and 500 patients, resulting in an increment by three times in the number of codes. MEDS-Tab gave efficient results in terms of both time and memory usage. @@ -343,75 +339,73 @@ a) 100 Patients **Table 1: 6,374 Codes, 2,065,608 Rows, Output Shape \[132,461, 6,374\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ----------- | ------------ | ----------- | -------- | -| 0m39.426s | 5,271.85 MB | 14,791.83 MB | 362 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m39.426s | 5,271 MB | 14,791 MB | 362 MB | meds_tab | b) 500 Patients **Table 2: 18,314 Codes, 8,737,355 Rows, Output Shape \[565,014, 18,314\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ----------- | ------------ | ----------- | -------- | -| 3m4.435s | 8,335.44 MB | 15,102.55 MB | 1,326 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 3m4.435s | 8,335 MB | 15,102 MB | 1,326 MB | meds_tab | -## MIMIC-IV Dataset +### MIMIC-IV Dataset -______________________________________________________________________ - -MEDS-Tab, TSFresh, and Catabra were tested across three different scales on MIMIC_IV. +MEDS-Tab, `tsfresh`, and `catabra` were tested across three different patient scales on MIMIC-IV. a) 10 Patients -This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming TSFresh and Catabra in terms of both time and memory efficiency. +This table illustrates the efficiency of MEDS-Tab in processing a small subset of patients with extremely low computational cost and high data throughput, outperforming `tsfresh` and `catabra` in terms of both time and memory efficiency. **Table 3: 1,504 Codes, 23,346 Rows, Output Shape \[2,127, 1,504\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ------------ | ------------- | ----------- | -------- | -| 0m2.071s | 423.78 MB | 943.984 MB | 7 MB | meds_tab | -| 1m41.920s | 84,159.44 MB | 265,877.86 MB | 1 MB | tsfresh | -| 0m15.366s | 2,537.46 MB | 4,781.824 MB | 1 MB | catabra | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m2.071s | 423 MB | 943 MB | 7 MB | meds_tab | +| 1m41.920s | 84,159 MB | 265,877 MB | 1 MB | tsfresh | +| 0m15.366s | 2,537 MB | 4,781 MB | 1 MB | catabra | b) 100 Patients -For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to TSFresh and Catabra. The performance gap was further highlighted with an increased number of patients and codes. +The performance gap was further highlighted with an increased number of patients and codes. For a moderate patient count, MEDS-Tab demonstrated superior performance with significantly lower wall times and memory usage compared to `tsfresh` and `catabra`. **Table 4: 4,154 Codes, 150,789 Rows, Output Shape \[15,664, 4,154\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ------------- | ------------- | ----------- | -------- | -| 0m4.724s | 718.76 MB | 1,167.29 MB | 45 MB | meds_tab | -| 5m9.077s | 217,477.52 MB | 659,735.25 MB | 4 MB | tsfresh | -| 3m17.671s | 14,319.53 MB | 28,342.81 MB | 4 MB | catabra | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m4.724s | 718 MB | 1,167 MB | 45 MB | meds_tab | +| 5m9.077s | 217,477 MB | 659,735 MB | 4 MB | tsfresh | +| 3m17.671s | 14,319 MB | 28,342 MB | 4 MB | catabra | c) 500 Patients -Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of ???, we could not get results for Catabra and TSFresh. +Scaling further to 500 patients, MEDS-Tab maintained consistent performance, reinforcing its capability to manage large datasets efficiently. Because of the set time limit of 10 minutes, we could not get results for `catabra` and `tsfresh`. In comparison, MEDS-Tab processed the data in about 15 seconds, making it at least 40 times faster for the given patient scale. **Table 5: 48,115 Codes, 795,368 Rows, Output Shape \[75,595, 8,115\]** -| Wall Time | Avg Memory | Peak Memory | Output Size | Method | -| --------- | ----------- | ----------- | ----------- | -------- | -| 0m15.867s | 1,410.79 MB | 3,539.32 MB | 442 MB | meds_tab | +| Wall Time | Avg Memory | Peak Memory | Output Size | Method | +| --------- | ---------- | ----------- | ----------- | -------- | +| 0m15.867s | 1,410 MB | 3,539 MB | 442 MB | meds_tab | ______________________________________________________________________ -# XGBoost Performance +# Prediction Performance -## D.1 XGBoost Model Performance on MIMIC-IV Across Tasks +## XGBoost Model Performance on MIMIC-IV Tasks -Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. +Evaluating our tabularization approach for baseline models involved training XGBoost across a spectrum of binary clinical prediction tasks, using data from the MIMIC-IV database. These tasks encompassed diverse outcomes such as mortality predictions over different intervals, readmission predictions, and lengths of stay (LOS) in both ICU and hospital settings. Each task is characterized by its specific label and prediction time. For instance, predicting "30-day readmission" involves assessing whether a patient returns to the hospital within 30 days, with predictions made at the time of discharge. This allows input features to be derived from the entire duration of the patient's admission. In contrast, tasks like "In ICU Mortality" focus on predicting the occurrence of death using only data from the first 24 or 48 hours of ICU admission. Specifically, we use the terminology "Index Timestamp" to mean the timestamp such that no event included as input will occur later than this point. -Optimizing predictive accuracy and model performance necessitated employing varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. +We optimize predictive accuracy and model performance by using varied window sizes and aggregations of patient data. This approach allows us to effectively capture and leverage the temporal dynamics and clinical nuances inherent in each prediction task. -### 1.1 XGBoost Time and Memory Profiling on MIMIC-IV +### 1. XGBoost Time and Memory Profiling on MIMIC-IV A single XGBoost run was completed to profile time and memory usage. This was done for each `$TASK` using the following command: -``` +```console meds-tab-xgboost MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -441,47 +435,47 @@ aggs: - value/max ``` -Since this includes every window size and aggregation, it is the most expoensive to run. The runtimes and memory usage are reported below. +Since this includes every window size and aggregation, it is the most expensive to run. The runtimes and memory usage are reported below. -#### 1.1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks +#### 1.1 XGBoost Runtimes and Memory Usage on MIMIC-IV Tasks -| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | -| ------------------------------- | ----------------- | --------- | --------- | --------- | ---------------- | ----------------- | -| Post-discharge 30 day Mortality | Discharge | 2m59.807s | 3m38.285s | 0m38.274s | 9036.728 | 11955.312 | -| Post-discharge 1 year Mortality | Discharge | 5m16.958s | 6m10.065s | 0m58.964s | 10803.670 | 12330.355 | -| 30 day Readmission | Discharge | 2m30.609s | 3m3.836s | 0m39.415s | 13198.832 | 18676.605 | -| In ICU Mortality | Admission + 24 hr | 0m38.275s | 1m3.656s | 0m13.198s | 1711.942 | 2985.699 | -| In ICU Mortality | Admission + 48 hr | 0m34.669s | 1m1.389s | 0m12.742s | 1613.256 | 2770.438 | -| In Hospital Mortality | Admission + 24 hr | 2m8.912s | 2m41.180s | 0m31.707s | 9071.615 | 12055.980 | -| In Hospital Mortality | Admission + 48 hr | 1m54.025s | 2m25.322s | 0m28.925s | 8857.783 | 12370.898 | -| LOS in ICU > 3 days | Admission + 24 hr | 2m2.689s | 2m37.196s | 0m28.110s | 4650.008 | 5715.484 | -| LOS in ICU > 3 days | Admission + 48 hr | 1m44.434s | 2m18.558s | 0m23.825s | 4453.363 | 5577.164 | -| LOS in Hospital > 3 days | Admission + 24 hr | 6m4.884s | 7m5.025s | 1m4.335s | 11011.710 | 12223.449 | -| LOS in Hospital > 3 days | Admission + 48 hr | 6m9.587s | 7m12.853s | 1m3.858s | 10703.064 | 11829.742 | +| Task | Index Timestamp | Real Time | User Time | Sys Time | Avg Memory (MiB) | Peak Memory (MiB) | +| ------------------------------- | ----------------- | --------- | --------- | -------- | ---------------- | ----------------- | +| Post-discharge 30 day Mortality | Discharge | 2m59s | 3m38s | 0m38s | 9,037 | 11,955 | +| Post-discharge 1 year Mortality | Discharge | 5m16s | 6m10s | 0m59s | 10,804 | 12,330 | +| 30 day Readmission | Discharge | 2m30s | 3m3s | 0m39s | 13,199 | 18,677 | +| In ICU Mortality | Admission + 24 hr | 0m38s | 1m3s | 0m13s | 1,712 | 2,986 | +| In ICU Mortality | Admission + 48 hr | 0m34s | 1m1s | 0m13s | 1,613 | 2,770 | +| In Hospital Mortality | Admission + 24 hr | 2m8s | 2m41s | 0m32s | 9,072 | 12,056 | +| In Hospital Mortality | Admission + 48 hr | 1m54s | 2m25s | 0m29s | 8,858 | 12,371 | +| LOS in ICU > 3 days | Admission + 24 hr | 2m3s | 2m37s | 0m28s | 4,650 | 5,715 | +| LOS in ICU > 3 days | Admission + 48 hr | 1m44s | 2m18s | 0m24s | 4,453 | 5,577 | +| LOS in Hospital > 3 days | Admission + 24 hr | 6m5s | 7m5s | 1m4s | 11,012 | 12,223 | +| LOS in Hospital > 3 days | Admission + 48 hr | 6m10s | 7m12s | 1m4s | 10,703 | 11,830 | -#### 1.1.2 MIMIC-IV Task Specific Training Cohort Size +#### 1.2 MIMIC-IV Task Specific Training Cohort Size To better understand the runtimes, we also report the task specific cohort size. | Task | Index Timestamp | Number of Patients | Number of Events | | ------------------------------- | ----------------- | ------------------ | ---------------- | -| Post-discharge 30 day Mortality | Discharge | 149014 | 356398 | -| Post-discharge 1 year Mortality | Discharge | 149014 | 356398 | -| 30 day Readmission | Discharge | 17418 | 377785 | -| In ICU Mortality | Admission + 24 hr | 7839 | 22811 | -| In ICU Mortality | Admission + 48 hr | 6750 | 20802 | -| In Hospital Mortality | Admission + 24 hr | 51340 | 338614 | -| In Hospital Mortality | Admission + 48 hr | 47231 | 348289 | -| LOS in ICU > 3 days | Admission + 24 hr | 42809 | 61342 | -| LOS in ICU > 3 days | Admission + 48 hr | 42805 | 61327 | -| LOS in Hospital > 3 days | Admission + 24 hr | 152126 | 360208 | -| LOS in Hospital > 3 days | Admission + 48 hr | 152120 | 359020 | - -### 1.2 MIMIC-IV Sweep +| Post-discharge 30 day Mortality | Discharge | 149,014 | 356,398 | +| Post-discharge 1 year Mortality | Discharge | 149,014 | 356,398 | +| 30 day Readmission | Discharge | 17,418 | 377,785 | +| In ICU Mortality | Admission + 24 hr | 7,839 | 22,811 | +| In ICU Mortality | Admission + 48 hr | 6,750 | 20,802 | +| In Hospital Mortality | Admission + 24 hr | 51,340 | 338,614 | +| In Hospital Mortality | Admission + 48 hr | 47,231 | 348,289 | +| LOS in ICU > 3 days | Admission + 24 hr | 42,809 | 61,342 | +| LOS in ICU > 3 days | Admission + 48 hr | 42,805 | 61,327 | +| LOS in Hospital > 3 days | Admission + 24 hr | 152,126 | 360,208 | +| LOS in Hospital > 3 days | Admission + 48 hr | 152,120 | 359,020 | + +### 2. MIMIC-IV Sweep The XGBoost sweep was run using the following command for each `$TASK`: -``` +```console meds-tab-xgboost --multirun \ MEDS_cohort_dir="path_to_data" \ task_name=$TASK \ @@ -521,38 +515,85 @@ Note that the XGBoost command shown includes `tabularization.window_sizes` and ` For a complete example on MIMIC-IV and for all of our config files, see the [MIMIC-IV companion repository](https://github.com/mmcdermott/MEDS_TAB_MIMIC_IV). -#### 1.2.1 XGBoost Performance on MIMIC-IV - -| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | -| ------------------------------- | ----------------- | ------------ | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | -| Post-discharge 30 day Mortality | Discharge | 0.9347633541 | 1371 | 5712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | -| Post-discharge 1 year Mortality | Discharge | 0.8979986449 | 289 | 10048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | -| 30 day Readmission | Discharge | 0.7076685566 | 303 | 9903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | -| In ICU Mortality | Admission + 24 hr | 0.6612338171 | 7059 | 3037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In ICU Mortality | Admission + 48 hr | 0.671986067 | 71 | 16112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In Hospital Mortality | Admission + 24 hr | 0.8119187166 | 43 | 18989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | -| In Hospital Mortality | Admission + 48 hr | 0.8100362943 | 678 | 7433 | \[1d,full\] | \[static/present,code/count,value/count\] | -| LOS in ICU > 3 days | Admission + 24 hr | 0.9455099633 | 30443 | 1624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | -| LOS in ICU > 3 days | Admission + 48 hr | 0.9667108784 | 2864 | 4332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.9434966078 | 94633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.9449770561 | 30880 | 1619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | +#### 2.1 XGBoost Performance on MIMIC-IV + +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Number of Included Codes\* | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ----- | -------------------------------- | -------------------------- | ---------------------- | --------------------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.935 | 1,371 | 5,712 | \[7d,full\] | \[code/count,value/count,value/min,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.898 | 289 | 10,048 | \[2h,12h,1d,30d,full\] | \[static/present,code/count,value/sum_sqd,value/min\] | +| 30 day Readmission | Discharge | 0.708 | 303 | 9,903 | \[30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| In ICU Mortality | Admission + 24 hr | 0.661 | 7,059 | 3,037 | \[12h,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In ICU Mortality | Admission + 48 hr | 0.673 | 71 | 16,112 | \[1d,7d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.812 | 43 | 18,989 | \[1d,full\] | \[static/present,code/count,value/sum,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.810 | 678 | 7,433 | \[1d,full\] | \[static/present,code/count,value/count\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.946 | 30,443 | 1,624 | \[2h,7d,30d\] | \[static/present,code/count,value/count,value/sum,value/sum_sqd,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.967 | 2,864 | 4,332 | \[2h,7d,30d\] | \[code/count,value/sum_sqd,value/max\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.943 | 94,633 | 912 | \[12h,1d,7d\] | \[code/count,value/count,value/sum_sqd\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.945 | 30,880 | 1,619 | \[1d,7d,30d\] | \[code/count,value/sum,value/min,value/max\] | - Number of Included Codes is based on Minimum Code Inclusion Frequency -- we calculated the number of resulting codes that were above the minimum threshold and reported that. -#### 1.2.2 XGBoost Optimal Found Model Parameters +#### 2.2 XGBoost Optimal Found Model Parameters Additionally, the model parameters from the highest performing run are reported below. -| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | -| ------------------------------- | ----------------- | -------------- | -------------- | ------------- | ------------ | -------------------- | ------------------------- | --------------------- | -------------- | -| Post-discharge 30 day Mortality | Discharge | 0.005630897092 | 0.03218837176 | 0.3741846464 | 0.5716492359 | 52.66844896 | 703 | 9 | 16 | -| Post-discharge 1 year Mortality | Discharge | 0.008978198787 | 0.086075240914 | 0.342564218 | 0.8994363088 | 75.94359197 | 858 | 9 | 11 | -| 30 day Readmission | Discharge | 0.005970244514 | 0.3591376982 | 0.3741846464 | 0.673450045 | 52.66844896 | 712 | 9 | 16 | -| In ICU Mortality | Admission + 24 hr | 0.03824348927 | 0.06183970736 | 0.2310791064 | 0.9947482627 | 88.53086045 | 513 | 7 | 14 | -| In ICU Mortality (first 48h) | Admission + 48 hr | 0.04373178504 | 0.04100575186 | 0.2888938852 | 0.9617417624 | 90.881739 | 484 | 5 | 14 | -| In Hospital Mortality | Admission + 24 hr | 0.02790651024 | 0.01319397229 | 0.0105408763 | 0.5673852112 | 11.22281297 | 454 | 6 | 9 | -| In Hospital Mortality | Admission + 48 hr | 0.01076063059 | 0.06007544254 | 0.1791900222 | 0.9641152835 | 83.69584368 | 631 | 7 | 13 | -| LOS in ICU > 3 days | Admission + 24 hr | 0.01203878234 | 0.08963582145 | 0.1367180869 | 0.6264012852 | 26.20493325 | 650 | 8 | 14 | -| LOS in ICU > 3 days | Admission + 48 hr | 0.01203878234 | 0.04882102808 | 0.1997059646 | 0.9608288859 | 83.9736355 | 615 | 7 | 13 | -| LOS in Hospital > 3 days | Admission + 24 hr | 0.008389745342 | 0.06656965098 | 0.2553069741 | 0.9886841026 | 89.89987526 | 526 | 5 | 14 | -| LOS in Hospital > 3 days | Admission + 48 hr | 0.00121145622 | 0.03018152667 | 0.02812771908 | 0.9671829656 | 8.657613623 | 538 | 8 | 7 | +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | ----- | ------ | ----- | --------- | -------------------- | ------------------------- | --------------------- | -------------- | +| Post-discharge 30 day Mortality | Discharge | 0.006 | 0.032 | 0.374 | 0.572 | 53 | 703 | 9 | 16 | +| Post-discharge 1 year Mortality | Discharge | 0.009 | 0.086 | 0.343 | 0.899 | 76 | 858 | 9 | 11 | +| 30 day Readmission | Discharge | 0.006 | 0.359 | 0.374 | 0.673 | 53 | 712 | 9 | 16 | +| In ICU Mortality | Admission + 24 hr | 0.038 | 0.062 | 0.231 | 0.995 | 89 | 513 | 7 | 14 | +| In ICU Mortality (first 48h) | Admission + 48 hr | 0.044 | 0.041 | 0.289 | 0.961 | 91 | 484 | 5 | 14 | +| In Hospital Mortality | Admission + 24 hr | 0.028 | 0.013 | 0.011 | 0.567 | 11 | 454 | 6 | 9 | +| In Hospital Mortality | Admission + 48 hr | 0.011 | 0.060 | 0.179 | 0.964 | 84 | 631 | 7 | 13 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.012 | 0.090 | 0.137 | 0.626 | 26 | 650 | 8 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.012 | 0.049 | 0.200 | 0.960 | 84 | 615 | 7 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.008 | 0.067 | 0.255 | 0.989 | 90 | 526 | 5 | 14 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.001 | 0.030 | 0.028 | 0.967 | 9 | 538 | 8 | 7 | + +## XGBoost Model Performance on eICU Tasks + +### eICU Sweep + +The eICU sweep was conducted equivalently to the MIMIC-IV sweep. Please refer to the MIMIC-IV Sweep subsection above for details on the commands and sweep parameters. + +For more details about eICU specific task generation and running, see the [eICU companion repository](https://github.com/mmcdermott/MEDS_TAB_EICU). + +#### 1. XGBoost Performance on eICU + +| Task | Index Timestamp | AUC | Minimum Code Inclusion Frequency | Window Sizes | Aggregations | +| ------------------------------- | ----------------- | ----- | -------------------------------- | ------------------------ | -------------------------------------------------------------- | +| Post-discharge 30 day Mortality | Discharge | 0.603 | 68,235 | \[12h,1d,full\] | \[code/count,value/sum_sqd,value/max\] | +| Post-discharge 1 year Mortality | Discharge | 0.875 | 3,280 | \[30d,365d\] | \[static/present,value/sum,value/sum_sqd,value/min,value/max\] | +| In Hospital Mortality | Admission + 24 hr | 0.855 | 335,912 | \[2h,7d,30d,365d,full\] | \[static/present,code/count,value/count,value/min,value/max\] | +| In Hospital Mortality | Admission + 48 hr | 0.570 | 89,121 | \[12h,1d,30d\] | \[code/count,value/count,value/min\] | +| LOS in ICU > 3 days | Admission + 24 hr | 0.783 | 7,881 | \[1d,30d,full\] | \[static/present,code/count,value/count,value/sum,value/max\] | +| LOS in ICU > 3 days | Admission + 48 hr | 0.757 | 1,719 | \[2h,12h,7d,30d,full\] | \[code/count,value/count,value/sum,value/sum_sqd,value/min\] | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.864 | 160 | \[1d,30d,365d,full\] | \[static/present,code/count,value/min,value/max\] | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.895 | 975 | \[12h,1d,30d,365d,full\] | \[code/count,value/count,value/sum,value/sum_sqd\] | + +#### 2. XGBoost Optimal Found Model Parameters + +| Task | Index Timestamp | Eta | Lambda | Alpha | Subsample | Minimum Child Weight | Number of Boosting Rounds | Early Stopping Rounds | Max Tree Depth | +| ------------------------------- | ----------------- | ----- | ------ | ----- | --------- | -------------------- | ------------------------- | --------------------- | -------------- | +| In Hospital Mortality | Admission + 24 hr | 0.043 | 0.001 | 0.343 | 0.879 | 13 | 574 | 9 | 14 | +| In Hospital Mortality | Admission + 48 hr | 0.002 | 0.002 | 0.303 | 0.725 | 0 | 939 | 9 | 12 | +| LOS in ICU > 3 days | Admission + 24 hr | 0.210 | 0.189 | 0.053 | 0.955 | 5 | 359 | 6 | 14 | +| LOS in ICU > 3 days | Admission + 48 hr | 0.340 | 0.393 | 0.004 | 0.900 | 6 | 394 | 10 | 13 | +| LOS in Hospital > 3 days | Admission + 24 hr | 0.026 | 0.238 | 0.033 | 0.940 | 46 | 909 | 5 | 11 | +| LOS in Hospital > 3 days | Admission + 48 hr | 0.100 | 0.590 | 0.015 | 0.914 | 58 | 499 | 10 | 9 | +| Post-discharge 30 day Mortality | Discharge | 0.003 | 0.0116 | 0.001 | 0.730 | 13 | 986 | 7 | 7 | +| Post-discharge 1 year Mortality | Discharge | 0.005 | 0.006 | 0.002 | 0.690 | 93 | 938 | 6 | 14 | + +#### 3. eICU Task Specific Training Cohort Size + +| Task | Index Timestamp | Number of Patients | Number of Events | +| ------------------------------- | ----------------- | ------------------ | ---------------- | +| Post-discharge 30 day Mortality | Discharge | 91,405 | 91,405 | +| Post-discharge 1 year Mortality | Discharge | 91,405 | 91,405 | +| In Hospital Mortality | Admission + 24 hr | 35,85 | 3,585 | +| In Hospital Mortality | Admission + 48 hr | 1,527 | 1,527 | +| LOS in ICU > 3 days | Admission + 24 hr | 12,672 | 14,004 | +| LOS in ICU > 3 days | Admission + 48 hr | 12,712 | 14,064 | +| LOS in Hospital > 3 days | Admission + 24 hr | 99,540 | 99,540 | +| LOS in Hospital > 3 days | Admission + 48 hr | 99,786 | 99,786 | From 7fe635acb4713462217bd85216d31be9588d56b1 Mon Sep 17 00:00:00 2001 From: Nassim Oufattole Date: Wed, 3 Jul 2024 04:42:29 +0000 Subject: [PATCH 46/46] cleaned up tables --- README.md | 24 +++++++++++++++--------- docs/source/profiling.md | 18 +++++++++--------- 2 files changed, 24 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index c3b7fec..086fc04 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,8 @@ This repository provides utilities and scripts to run limited automatic tabular ML pipelines for generic MEDS datasets. +______________________________________________________________________ + # Usage This repository consists of two key pieces: @@ -200,6 +202,8 @@ realize these constraints, which will explicitly involve summarizing the patient or future windows in time to produce a single row per patient with a consistent, logical set of columns (though there may still be missingness). +______________________________________________________________________ + # The MEDS-Tab Architecture In this section, we describe the MEDS-Tab architecture, specifically some of the pipeline choices we made to reduce memory usage and increase speed during the tabularization process and XGBoost tuning process. @@ -306,6 +310,8 @@ The final stage uses the processed and cached data to train an XGBoost model. Th - **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance. - **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window sizes, and the minimimum code inclusion frequency. +______________________________________________________________________ + # Computational Performance vs. Existing Pipelines Evaluating the computational overhead of tabularization methods is essential for assessing their efficiency and suitability for large-scale medical data processing. This section presents a comparative analysis of the computational overhead of MEDS-Tab with other systems like Catabra and TSFresh. It outlines the performance of each system in terms of wall time, memory usage, and output size, highlighting the computational efficiency and scalability of MEDS-Tab. @@ -341,7 +347,7 @@ a) 100 Patients | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m39.426s | 5,271 MB | 14,791 MB | 362 MB | meds_tab | +| 0m39s | 5,271 MB | 14,791 MB | 362 MB | meds_tab | b) 500 Patients @@ -349,7 +355,7 @@ b) 500 Patients | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 3m4.435s | 8,335 MB | 15,102 MB | 1,326 MB | meds_tab | +| 3m4s | 8,335 MB | 15,102 MB | 1,326 MB | meds_tab | ### MIMIC-IV Dataset @@ -363,9 +369,9 @@ This table illustrates the efficiency of MEDS-Tab in processing a small subset o | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m2.071s | 423 MB | 943 MB | 7 MB | meds_tab | -| 1m41.920s | 84,159 MB | 265,877 MB | 1 MB | tsfresh | -| 0m15.366s | 2,537 MB | 4,781 MB | 1 MB | catabra | +| 0m2s | 423 MB | 943 MB | 7 MB | meds_tab | +| 1m41s | 84,159 MB | 265,877 MB | 1 MB | tsfresh | +| 0m15s | 2,537 MB | 4,781 MB | 1 MB | catabra | b) 100 Patients @@ -375,9 +381,9 @@ The performance gap was further highlighted with an increased number of patients | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m4.724s | 718 MB | 1,167 MB | 45 MB | meds_tab | -| 5m9.077s | 217,477 MB | 659,735 MB | 4 MB | tsfresh | -| 3m17.671s | 14,319 MB | 28,342 MB | 4 MB | catabra | +| 0m5s | 718 MB | 1,167 MB | 45 MB | meds_tab | +| 5m9s | 217,477 MB | 659,735 MB | 4 MB | tsfresh | +| 3m17s | 14,319 MB | 28,342 MB | 4 MB | catabra | c) 500 Patients @@ -387,7 +393,7 @@ Scaling further to 500 patients, MEDS-Tab maintained consistent performance, rei | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m15.867s | 1,410 MB | 3,539 MB | 442 MB | meds_tab | +| 0m16s | 1,410 MB | 3,539 MB | 442 MB | meds_tab | ______________________________________________________________________ diff --git a/docs/source/profiling.md b/docs/source/profiling.md index 5a0c083..4ae644e 100644 --- a/docs/source/profiling.md +++ b/docs/source/profiling.md @@ -33,7 +33,7 @@ a) 100 Patients | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m39.426s | 5,271 MB | 14,791 MB | 362 MB | meds_tab | +| 0m39s | 5,271 MB | 14,791 MB | 362 MB | meds_tab | b) 500 Patients @@ -41,7 +41,7 @@ b) 500 Patients | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 3m4.435s | 8,335 MB | 15,102 MB | 1,326 MB | meds_tab | +| 3m4s | 8,335 MB | 15,102 MB | 1,326 MB | meds_tab | ### MIMIC-IV Dataset @@ -55,9 +55,9 @@ This table illustrates the efficiency of MEDS-Tab in processing a small subset o | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m2.071s | 423 MB | 943 MB | 7 MB | meds_tab | -| 1m41.920s | 84,159 MB | 265,877 MB | 1 MB | tsfresh | -| 0m15.366s | 2,537 MB | 4,781 MB | 1 MB | catabra | +| 0m2s | 423 MB | 943 MB | 7 MB | meds_tab | +| 1m41s | 84,159 MB | 265,877 MB | 1 MB | tsfresh | +| 0m15s | 2,537 MB | 4,781 MB | 1 MB | catabra | b) 100 Patients @@ -67,9 +67,9 @@ The performance gap was further highlighted with an increased number of patients | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m4.724s | 718 MB | 1,167 MB | 45 MB | meds_tab | -| 5m9.077s | 217,477 MB | 659,735 MB | 4 MB | tsfresh | -| 3m17.671s | 14,319 MB | 28,342 MB | 4 MB | catabra | +| 0m5s | 718 MB | 1,167 MB | 45 MB | meds_tab | +| 5m9s | 217,477 MB | 659,735 MB | 4 MB | tsfresh | +| 3m17s | 14,319 MB | 28,342 MB | 4 MB | catabra | c) 500 Patients @@ -79,6 +79,6 @@ Scaling further to 500 patients, MEDS-Tab maintained consistent performance, rei | Wall Time | Avg Memory | Peak Memory | Output Size | Method | | --------- | ---------- | ----------- | ----------- | -------- | -| 0m15.867s | 1,410 MB | 3,539 MB | 442 MB | meds_tab | +| 0m16s | 1,410 MB | 3,539 MB | 442 MB | meds_tab | ______________________________________________________________________