Can I read BoXHED2.0: Scalable boosting of dynamic survival analysis on EtoBox?

BoXHED2.0: Scalable boosting of dynamic survival analysis by Pakbin, Arash; Wang, Xiaochen; Mortazavi, Bobak J.; Lee, Donald K. K. is a scholarly article available to read on EtoBox.

What is BoXHED2.0: Scalable boosting of dynamic survival analysis about?

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.

Author

Pakbin, Arash; Wang, Xiaochen; Mortazavi, Bobak J.; Lee, Donald K. K.

Published

2021

Language

EN