# BoXHED2.0: Scalable boosting of dynamic survival analysis

arXiv: https://arxiv.org/abs/2103.12591 · HTML: https://mlsanity.com/paper/2103.12591/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates](https://mlsanity.com/paper/2006.14218/) (2020)
- [Transformation Forests](https://mlsanity.com/paper/1701.02110/) (2017)

## Built on by (most cited first, 6 of 6)

- [Nonparametric Conditional Local Independence Testing](https://mlsanity.com/paper/2203.13559/) (2022)
- [Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards](https://mlsanity.com/paper/2110.08949/) (2021)
- [FPBoost: Fully Parametric Gradient Boosting for Survival Analysis](https://mlsanity.com/paper/2409.13363/) (2024)
- [Realtime, multimodal invasive ventilation risk monitoring using language models and BoXHED](https://mlsanity.com/paper/2410.03725/) (2024)
- [MedM2T: A MultiModal Framework for Time-Aware Modeling with Electronic Health Record and Electrocardiogram Data](https://mlsanity.com/paper/2510.27321/) (2025)
- [Random Hazard Forests](https://mlsanity.com/paper/2608.21597/) (2026)

## Abstract (arXiv)

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.