Welcome to the repository for "Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis", to appear at AISTATS 2022.

Getting started

Create a virtual environment and activate it:

python -m venv desurv_env
source desurv_env/bin/activate

Install dependencies:

python -m pip install -r requirements.txt

Access the source code:

cd src

Use DeCDF for CDF/density estimation:

python -m jupyter notebook fig2.ipynb

Train a DeSurv model:

python desurv.py

Additional info:

The core code components of DeCDF/DeSurv can be found in classes.py. The ODESurvSingle class implements a DeSurv model for the single-risk setting. The ODESurvMultiple class implements a DeSurv model for the competing-risk setting. desurv.py illustrates how a DeSurv model can be trained and is designed to be easily adaptable to a typical user's problem setting. fig2.ipynb illustrates the basic concept behind DeCDF and hence DeSurv in an interactive setting.

Acknowledgements

We would like to acknowledge the pycox library of Kvamme et al. for its excellent dataset, model and evaluation provisions which proved very useful when undertaking this project.