Skip to content

Latest commit

 

History

History
58 lines (48 loc) · 2.82 KB

README.md

File metadata and controls

58 lines (48 loc) · 2.82 KB

BNN-ISD

Offical Code Release for "Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction"

This model can be used for (1) select relevant features for survival analysis, and (2) compute credible intervals for the predicted survival curve. This model also excels in both discrimination and calibration performance, allowing it to accurately predict the survival curve for individuals.

Environment Setup

Please install the following packages before running the code:

  • Python>=3.7
  • CPU or GPU
  • Other packages can be installed with the following instruction: 
    $ pip install -r requirements.txt

Quick start

Make Folder/Packages as Source Code

Before running the code, please make sure that the SurvivalEVAL folder is treated as source code

  • For Pycharm (or other IDEs)):
    • Right-click on the SurvivalEVAL folder
    • Select Mark Directory as -> Sources Root
  • For terminal:
    • Add the following lines to your ~/.bashrc file: 
      export PYTHONPATH="${PYTHONPATH}:/your-path-to/SurvivalEVAL"
    • Run the following command: 
      $ source ~/.bashrc

Running

Running the code with the following command.

$ python3 run_models.py --dataset Synthetic-II --model BayesianHorseshoeMTLR --lr 0.00008

Or run the bash script:

$ bash run.sh

Datasets

The datasets we tested in the paper are Synthetic-I, Synthetic-II, SUPPORT, NACD, and MIMIC.

For Synthetic-I and Synthetic-II, we generated them using the code in datasets.py. For SUPPORT, we directly download and process the data from the website, which can also be found in datasets.py. That means user can directly run the code on these three datasets.

For NACD and MIMIC, we cannot directly provide the data due to the privacy issue.

If you are interested in using the NACD dataset you can access the NACD data from the Patient Specific Survival Prediction (PSSP) website under "Public Predictors" or use this direct download link. And then rename the file to NACD_Full.csv and put it in the data/NACD/ folder.

If you are interested in using the MIMIC dataset, you can access the MIMIC data from the MIMIC website under "Accessing MIMIC-IV v2.0", or directly access this MIMIC-IV Version 2.0. You first need to go through the ethic process, and once you have done that, you can go to the BigQuery and process the data using the json script MIMIC_IV_V2.0.json in the data/MIMIC/ folder. And further process the data using the code in MIMIC_IV_V2.0_preprocess.py.