mlsanityData to 7 Oct 2026

BoXHED2.0: Scalable boosting of dynamic survival analysis

arXiv 2103.12591 ↗First posted 23 Mar 2021cs.LG stat.MLcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

4 of 24 references.

References · 4

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2017Multitask learning and benchmarking with clinical time series datacited by 287
  3. 2020BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatescited by 7
  4. 2017Transformation Forestscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 1.

6 papers.

  1. 2022Nonparametric Conditional Local Independence Testingcited by 7
  2. 2021Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazardscited by 6
  3. 2024FPBoost: Fully Parametric Gradient Boosting for Survival Analysiscited by 4
  4. 2024Realtime, multimodal invasive ventilation risk monitoring using language models and BoXHEDcited by 0
  5. 2025MedM2T: A MultiModal Framework for Time-Aware Modeling with Electronic Health Record and Electrocardiogram Datacited by 0
  6. 2026Random Hazard Forestscited by 0