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)
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!
You must have the csv files of eICU dataset on your local machine
For Feedforward Network and LSTM:
The content of this repository can be divide into two parts:
Here are the required steps to create the benchmark. The eICU dataset CSVs should be available on the disk.
git clone https://github.com/mostafaalishahi/eICU_Benchmark.git
cd eICU_Benchmark
python data_extraction_root.py
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)
All the desired settings for the training experiments are in the config.py file if you wish to change something.
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.
The baseline experiments can be ran by running
python bash_baseline.py
The LSTM experiments can be ran by running
python bash.py