ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
The source code for ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Our paper can be found here. Thanks for your interest in our work.
Visualization Tool
Welcome to try the prototype of our visualization tool (AdaCare):
http://47.93.42.104/215 (Cause of death: CVD)
http://47.93.42.104/318 (Cause of death: GI disease)
http://47.93.42.104/616 (Cause of death: Other)
http://47.93.42.104/265 (Cause of death: GI disease)
http://47.93.42.104/812 (Cause of death: Cachexia)
http://47.93.42.104/455 (Cause of death: CVD)
http://47.93.42.104/998 (Alive)
http://47.93.42.104/544 (Alive)
AdaCare can be found here, which is our another work in AAAI-2020.
Welcome to test the prototype of our visualization tool. The clinical hidden status is built by our latest representation learning model ConCare. The internationalised multi-language support will be available soon.
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.