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

Code for the papers:

Chauhan, V. K., Thakur, A., O’Donoghue, O., Rohanian, O., Molaei, S., & Clifton, D. A. (2024). Continuous patient state attention model for addressing irregularity in electronic health records. BMC Medical Informatics and Decision Making, 24(1), 117.

Chauhan, V. K., Thakur, A., O'Donoghue, O., & Clifton, D. A. (2022). COPER: Continuous patient state perceiver. In 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 1-4). IEEE.

Prerequisites

Install torchdiffeq from https://github.com/rtqichen/torchdiffeq.

Experiment

You need mimic-iii mimic-iii and Physionet Challenge 2012 datasets.

To run different experiments, you can use the following shell scripts:

• run_irregular_mimic.sh: to study irregularity on mimic dataset.
• run_irregular_physionet.sh: to study irregularity on physionet dataset.
• run_exp_normal.sh: to run Perceiver and baselines (without irregularity).
• run_perceiver_latents.sh: to study the effect of number of latents on the Perceiver and compare with Transformer.

Citations:

@article{chauhan2024continuous,
  title={Continuous patient state attention model for addressing irregularity in electronic health records},
  author={Chauhan, Vinod Kumar and Thakur, Anshul and O’Donoghue, Odhran and Rohanian, Omid and Molaei, Soheila and Clifton, David A},
  journal={BMC Medical Informatics and Decision Making},
  volume={24},
  number={1},
  pages={117},
  year={2024},
  publisher={Springer}
}

@inproceedings{Chauhan2022a,
  title={COPER: Continuous Patient State Perceiver},
  author={Chauhan, Vinod Kumar and Thakur, Anshul and O'Donoghue, Odhran and Clifton, David A},
  booktitle={IEEE International Conference on Biomedical and Health Informatics},
  year={2022},
  url={https://arxiv.org/abs/2208.03196}
}

Neural ODE implementations based on [Yulia Rubanova].