BoXHED2.0: Scalable boosting of dynamic survival analysis
This provides a scalable tool for researchers and practitioners in fields like healthcare and finance dealing with complex survival data, though it is incremental as it builds on existing boosting methods.
The paper tackles the challenge of survival analysis with time-dependent covariates by introducing BoXHED2.0, a Python package that uses tree-boosting for nonparametric hazard estimation, achieving scalability comparable to parametric models through C++ implementation and GPU/CPU support.
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.