https://github.com/epistasislab/interpret_ehr

Interpretation of machine learning predictions for patient outcomes in electronic health records
https://github.com/epistasislab/interpret_ehr

Last synced: 6 months ago
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Interpretation of machine learning predictions for patient outcomes in electronic health records

• Host: GitHub
• URL: https://github.com/epistasislab/interpret_ehr
• Owner: EpistasisLab
• License: gpl-3.0
• Created: 2019-03-13T22:03:08.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-11-19T15:11:43.000Z (about 6 years ago)
• Last Synced: 2025-07-09T04:38:10.413Z (6 months ago)
• Language: Jupyter Notebook
• Size: 4.78 MB
• Stars: 9
• Watchers: 4
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
This repo contains the analysis for the paper 

["Interpretation of machine learning predictions for patient outcomes in electronic health records"](https://arxiv.org/abs/1903.12074)

by William La Cava, Chris Bauer, Jason H. Moore, and Sarah A. Pendergrass.

 - the `analysis/` folder contains the code to run the experiments. 

 - the `notebooks/` folder contains the results analysis.

 - the `results/` folder contains the raw results files.

[Here is a link](https://drive.google.com/file/d/0B1iHrZwOcNdaLUNrdkJ6bEZlZm9xUXdOaTZ1clFtaEZlZHZv/view?usp=sharing) to my AMIA presentation.