HealthGen: Conditional EHR Time Series Generation

This repository contains the implementation of the HealthGen model, a generative model to synthesize realistic EHR time series data with missingness.

Installation

1. Clone the repo with: git clone --recurse-submodules git@github.com:simonbing/HealthGen.git.

2. Navigate to the /healthgen directory and install the dependencies by running: pip install requirements.txt.

3. Add the HealthGen module to your PYTHONPATH by running export PYTHONPATH=$PYTHONPATH:/path/to/HealthGen/healthgen.

4. Optionally, setup wandb, a useful tool for experiment tracking, which is integrated into our pipeline. After setting up a free account, add your credentials and the desired project name for the placeholders wandb_user and wandb_project in the code.

Data Access

We utilize the MIMIC-III data set for the training and evaluation of our generative model, which is publicly available to credentialed users.

To extract an intermediate representation of the EHR time series data, we utilize a slightly modified version of MIMIC-Extract, which is automatically cloned if you followed the instructions for installation. To extract the intermediate tables of the data required for our pipeline, follow the steps 1-4 in the instructions of MIMIC-Extract. In addition to the standard flags, you can set the sampling frequency (e.g. to 15 minutes) by calling: python mimic_direct_extract.py --time_step 15 ...

After the extraction has finished (extracting all patients can take several hours on a machine with around 50 GB of memory), you should obtain four tables with the extracted patient data. This is the input data for our experimental pipeline.

Use

The main components of the pipeline can be run independently: data querying and processing from the database, training a generative model, and evaluation.

To run the entire experimental pipeline, i.e. extract the time series from the intermediate tables, train a generative model and run the resulting evaluation, run:

main.py 
--input_vitals /path/to/vitals/table 
--input_outcomes /path/to/outcomes/table
--input_static /path/to/static/table
--gen_model healthgen
--evaluation grud
--out_path /path/to/save/results

For more information on all available flags, run main.py --helpfull, and see the comments in the code for additional information.

License

MIT License

Authors

Simon Bing, Andrea Dittadi, Stefan Bauer, Patrick Schwab