Home

Awesome

STraTS: Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series

This repo contains an official re-implementation of STraTS in pytorch. <br> Paper: https://arxiv.org/pdf/2107.14293.pdf

Important changes

Conda env setup

conda create -n strats python=3.10.9
source activate strats
pip install pytz pandas tqdm matplotlib 
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
pip install transformers==4.35.2
pip install scikit-learn==1.2.2

Dataset preprocessing

Download PhysioNet2012 dataset from https://physionet.org/content/challenge-2012/1.0.0/. <br> Download MIMIC-III from https://physionet.org/content/mimiciii/1.4/, <br> Update "RAW_DATA_PATH" variable in the preprocessing scripts and run them.

python preprocess_physionet_2012.py
python preprocess_mimic_iii_large.py

Training and evaluation

The shell script run_main.sh contains the commands for training and evaluating each of the supported models.

chmod +x run_main.sh
./run_main.sh

Results from run_main.sh

image

Citation

If you found this work useful, please cite our paper:

@article{tipirneni2022self,
  title={Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series},
  author={Tipirneni, Sindhu and Reddy, Chandan K},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  volume={16},
  number={6},
  pages={1--17},
  year={2022},
  publisher={ACM New York, NY}
}