ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

The source code for ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

Thanks for your interest in our work. More details about the experiment, case study and visualization system will be released before AAAI2020.

Requirements

• Install python, pytorch. We use Python 3.7.3, Pytorch 1.1.
• 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 decompensation 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/ directory.

Run ConCare

All the hyper-parameters and steps are included in the .ipynb file, you can run it directly.