BoXHED2.0: Scalable boosting of dynamic survival analysis
Machine Learning
2023-09-08 v5 Machine Learning
Abstract
Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.
Keywords
Cite
@article{arxiv.2103.12591,
title = {BoXHED2.0: Scalable boosting of dynamic survival analysis},
author = {Arash Pakbin and Xiaochen Wang and Bobak J. Mortazavi and Donald K. K. Lee},
journal= {arXiv preprint arXiv:2103.12591},
year = {2023}
}
Comments
27 pages