libsurv

Introduction

A library of efficient survival analysis models, including DeepCox, HitBoost, CEBoost and EfnBoost methods.

• DeepCox: Deep cox proportional hazard model implemented by tensorflow. It's exactly the same as TFDeepSurv.
• HitBoost: Survival analysis via a multi-output gradient boosting decision tree method.
• EfnBoost: Optimized cox proportional hazard model via an objective function of Efron approximation.
• CEBoost: Adding convex function approximated concordance index in EfnBoost to adjust risk ranking.

Enhancement

• comprehensive document
• python package distribution

Installation

# in the directory where `setup.py` is located
ls
# install via pip or pip3 (only support for python>=3.5)
pip3 install .

Usage

Usage of DeepCox, EfnBoost, CEBoost and HitBoost are provided in Jupyter Notebooks.

Hyper-parameters tuning can refer to libsurv/bysopt.

Citation

If you would like to cite our package, some reference papers are listed below:

• HitBoost(Accepted by IEEE-Access): HitBoost: Survival Analysis via a Multi-output Gradient Boosting Decision Tree method.
• EfnBoost(Accepted by IEEE-TBME): Optimizing Survival Analysis of XGBoost for Ties to Predict Prognostic Status of Breast Cancer
• DeepCox(Under Review): Deep Survival Learning for Predicting the Overall Survival in Breast Cancer using Clinical and Follow-up Data