LGTRL-DE: Local and Global Temporal Representation Learning with Demographic Embedding for In-hospital Mortality Prediction

The source code for LGTRL-DE: Local and Global Temporal Representation Learning with Demographic Embedding for In-hospital Mortality Prediction.

Thanks for your interest in our work.

Requirements

• We use Python 3.6.6, Keras 2.3.1.
• If you plan to use GPU computation, install CUDA

Data preparation

We do not provide the MIMIC-III data itself. You must acquire the data yourself from https://mimic.physionet.org/. Specifically, download the CSVs. To run in-hospital mortality prediction task on MIMIC-III bechmark dataset, you should first build benchmark dataset according to https://github.com/YerevaNN/mimic3-benchmarks/.

After building the in-hospital mortality dataset, please save the files in in-hospital-mortality directory to /data/row_data/ directory.

Run LGTRL-DE

1. Clone the repo.

   git clone https://github.com/Mengjielf/LGTRL-DE/
   cd LGTRL-DE/
   

2. Run the command to obtain processed data. The processed data are saved in /data/processed_data/ directory.

   python data_process.py
   

3. The trained models are saved in /5_fold_results/mimic_models/ directory. Run the following command to test model.

   CUDA_VISIBLE_DEVICES=0 python test.py