ICLR'23 ManyDG Paper

• ManyDG: Many-domain Generalization for Healthcare Applications
• Data, Processing Scripts, Baselines and Model Codes

15-minute explanation video is provided in [YouTube] https://www.youtube.com/watch?v=xKOSiauQtwc

1. Folder Tree

• data/
  • drugrec/
    • this is the folder stores data for drug recommendation task
    • data_process.py: this file is used for processing MIMIC-III data for the task
    • before running the scripts, please refer to https://physionet.org/content/mimiciii/1.4/ for downloading the latest version of MIMIC data
    • refer to https://github.com/sjy1203/GAMENet and https://github.com/ycq091044/SafeDrug for trouble shooting
  • Seizure/
  • sleep/
    • this is the data folder for the processed sample-based Sleep-EDF cassette portion.
    • sleep_edf_process.py: the processing file for sleep-edf (using multiprocess package for parallel programming)
    • for downloading the Sleep-EDF data, please refere to https://www.physionet.org/content/sleep-edfx/1.0.0/
  • hospitalization/
    • this is the folder for the processed eICU data
    • eICU_process_step1.py: the first processing step of eICU data
    • eICU_process_step2.py: the second processing step of eICU data
    • for downloading eICU data, please refer to https://eicu-crd.mit.edu/
  • idxFile/
    • this folder stores the code mapping of different event types in eICU dataset. They will be generated from eICU_process_step1.py and eICU_process_step2.py. They are used to assign feature dimensions for hospitalization prediction task
• log/, pre-trained/
  • this two folders stores the automatically generated running logs and pre-trained models
• model.py
  • this file contains all the backbone and Base models for running the experiments
• run_drugrec/
  • model_drugrec.py: This file inherit the Base model and other model utility functions from model.py and support the drug recommendaton task
  • run_drugrec.py: This is the entry of drug recommendation task, specifiying the data loader and other initialization steps.
  • utils_drugrec.py: This file provides the data split and loading files.
• run_hospitalization/
  • model_hospitalization.py: This file inherit the Base model and other model utility functions from model.py and support the hospitalization prediction task
  • run_hospitalization.py: This is the entry of hospitalization prediction task, specifiying the data loader and other initialization steps.
  • utils_hospitalization.py: This file provides the data split and loading files.
• run_seizure/
  • model_seizure.py: This file inherit the Base model and other model utility functions from model.py and support the seizure detection task
  • run_seizure.py: This is the entry of seizure detection task, specifiying the data loader and other initialization steps.
  • utils_seizure.py: This file provides the data split and loading files.
• run_sleep/
  • model_sleep.py: This file inherit the Base model and other model utility functions from model.py and support the sleep staging task
  • run_sleep.py: This is the entry of sleep stagin task, specifiying the data loader and other initialization steps.
  • utils_sleep.py: This file provides the data split and loading files.

2. How to run the code

• Models in the codebase
  • base: backbone + non-linear prediction head
  • dev: our ManyDG model!
  • condadv: check out the paper http://proceedings.mlr.press/v70/zhao17d.html
  • DANN: https://www.jmlr.org/papers/volume17/15-239/15-239.pdf
  • IRM: https://arxiv.org/abs/1907.02893
  • SagNet: https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.html
  • PCL: https://openaccess.thecvf.com/content/CVPR2022/html/Yao_PCL_Proxy-Based_Contrastive_Learning_for_Domain_Generalization_CVPR_2022_paper.html
  • MLDG: https://ojs.aaai.org/index.php/AAAI/article/view/11596
• for seizure detection

cd ./ManyDG
# obtain the Seizure data first
python run_seizure/run_seizure.py --model [MODEL] --cuda [WHICH GPU] --N_vote [DEFAULT 5] --N_pat [N_OF_PAT] --epochs [EPOCHS]

• for sleep staging

cd ./ManyDG
python data/sleep/sleep_edf_process.py
python run_sleep/run_sleep.py --model [MODEL] --cuda [WHICH GPU] --N_pat [N_OF_PAT] --epochs [EPOCHS]

• for drug recommendation

cd ./ManyDG
python data/drugrec/data_processing.py
python run_drugrec/run_drugrec.py --model [MODEL] --cuda [WHICH GPU] --N_pat [N_OF_PAT] --epochs [EPOCHS]

• for hospitalization prediction

cd ./ManyDG
python eICU_process_step1.py
python eICU_process_step2.py
python run_hospitalization/run_hospitalization.py --model [MODEL] --cuda [WHICH GPU] --N_pat [N_OF_PAT] --epochs [EPOCHS]

3. Citation

@inproceedings{yang2023manydg,
    title = {ManyDG: Many-domain Generalization for Healthcare Applications},
    author = {Yang, Chaoqi and Westover, M. Brandon and Sun, Jimeng},
    booktitle = {The 11th International Conference on Learning Representations, {ICLR} 2023},
    year = {2023}
}