This repository contains the code used for Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit (https://dl.acm.org/doi/10.1145/3450439.3451860).

Implementation of Deep Patient Representation of Clinical Notes at Intensive Care Unit for Multi-Task Prediction

A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction

Time-Sensitive Deep Learning for ICU Outcome Prediction Without Variable Selection or Cleaning.

FDSML Course Project 2020/21

Standard tools to compare and evaluate mortality forecasting methods

Repo of the Fleming project (dynamic prediction of mortality threat)

A Machine Learning Approach for Predicting the Death Time and Mortality

Code and Datasets for the paper "An Interpretable Risk Prediction Model for Healthcare with Pattern Attention", published on BMC Medical Informatics and Decision Making.

Continuous patient state attention model for addressing irregularity in electronic health records

COVID-19 Critical Event Prediction Tools

A RNN Predictive Model for COVID-19 mortality prediction.

implementation of the method presented in "Minimax Risk Classifiers for Mislabeled Data: a Study on Patient Outcome Prediction Tasks".

Predicting Mortality among a Cohort of patients with Heart Failure

Schätzung und Prognose der Sterblichkeit der britischen Bevölkerung (Männer und Frauen) unter Verwendung von zwei verschiedene Modelle: das Modell von Lee-Carter und das Modell von Cairns Blake Dowd (CBD)

This is implementation of my Masters thesis work and aims at predicting the patient mortality from the MIMIC-III v1.4 Database

Stata PRISM Score Calculation

This repository includes the code and data used to produce the analysis of placebo tests to measure excess post-disaster mortality.

Joint work product with co-authors Chelsea Jin and Matthew Ye for at the virtual ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop held on Sept. 22-25, 2020. Data engineering support from Nick Barbour.