Deep Cox Mixtures
Chirag Nagpal1,2 Steve Yadlowsky1, Negar Rostamzadeh1 and Katherine Heller1
1Google Brain Team, 2Carnegie Mellon University
❗ ⚠️ ❗ ⚠️ ❗ ⚠️ ❗ ⚠️ ❗ IMPORTANT NOTE❗ ⚠️ ❗ ⚠️ ❗ ⚠️ ❗ ⚠️ ❗ ⚠️ ❗ Deep Cox Mixtures now has a more stable
pytorch
implementation here:
https://autonlab.github.io/auton-survival/models/dcm/
tensorflow
version is no longer supported. Please use the version above. The repository is kept for legacy purposes.
This repository contains code for the MLHC 2021 paper:
Deep Cox Mixtures for Survival Regression
Installation
To download and run Deep Cox Mixtures:
foo@bar:~$ git clone https://github.com/chiragnagpal/deep_cox_mixtures.git
foo@bar:~$ cd deep_cox_mixtures
foo@bar:~$ pip install -r requirements.txt
Usage
To run DCM on a standard survival analysis dataset like SUPPORT, please see the following example notebook:
To run the original experiments from the paper, please use:
from dcm import deep_cox_mixture
results = deep_cox_mixture.experiment(dataset='SUPPORT', prot_att='race', groups=('white', 'other'))
deep_cox_mixture.display_results(results)
Requirements
dcm
depends on tensorflow2
and scikit-survival
,
Running baseline models for comparison requires lifelines
, pycox
and dsm
Citing
Please cite using the following bib-entry:
@article{nagpal2021dcm,
title={Deep Cox mixtures for survival regression},
author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
journal={Machine Learning for Healthcare Conference},
year={2021}
organization={PMLR}
}