/eICU_Benchmark

Primary LanguagePython

eICU Benhmarks

Reference

Seyedmostafa Sheikhalishahi and Vevake Balaraman and Venet Osmani. "Benchmarking machine learning models on eICU critical care dataset" available on arXiv (https://arxiv.org/abs/1910.00964v2)

Citation

If you use this code or these benchmarks in your research, please cite the following publication.

@misc{sheikhalishahi2019benchmarking, title={Benchmarking machine learning models on multi-centre eICU critical care dataset}, author={Seyedmostafa Sheikhalishahi and Vevake Balaraman and Venet Osmani}, year={2019}, eprint={1910.00964}, archivePrefix={arXiv}, primaryClass={cs.LG} }

Be sure to cite eICU paper!

Requirements

You must have the csv files of eICU dataset on your local machine

Packages

  • numpy==1.15.0
  • scipy==1.2.0
  • scikit-learn==0.21.2
  • pandas==0.24.1

For Feedforward Network and LSTM:

  • Keras==2.2.4

Structure

The content of this repository can be divide into two parts:

  • data extraction
  • running the models (baselines, LSTM)

How to Build this benchmark

Data extraction

Here are the required steps to create the benchmark. The eICU dataset CSVs should be available on the disk.

  1. Clone the repository.

git clone https://github.com/mostafaalishahi/eICU_Benchmark.git

cd eICU_Benchmark

  1. The following command generates one directory per each patient and writes patients demographics into pats.csv, the items extracted from Nursecharting into nc.csv and the lab items into lab.csv and then converts these three csv files into one timeseries.csv for each patient. you will have one csv file with all the patients data in a time-series manner for all the four tasks.

python data_extraction_root.py

Run the models

  1. Before going to run the experiment you need to set the desired configuration in the bash.py file (e.g. which tasks to choose with with settings)

  2. All the desired settings for the training experiments are in the config.py file if you wish to change something.

  3. The experiments are divided into two scripts for baseline and for the LSTM. In the both scripts there are arguments related to task, numerical, categorical, artificial neural networks, one-hot encoding, and mortality window data. Those arguments can be provided as binary and for mortality window we consider the first 24 and 48 hours of the admission data.

  4. The baseline experiments can be ran by running

python bash_baseline.py

  1. The LSTM experiments can be ran by running

python bash.py