# Random Hazard Forests

arXiv: https://arxiv.org/abs/2608.21597 · HTML: https://mlsanity.com/paper/2608.21597/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [Deep Recurrent Survival Analysis](https://mlsanity.com/paper/1809.02403/) (2018)
- [Deep Learning for Patient-Specific Kidney Graft Survival Analysis](https://mlsanity.com/paper/1705.10245/) (2017)
- [DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values](https://mlsanity.com/paper/1908.02337/) (2019)
- [BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates](https://mlsanity.com/paper/2006.14218/) (2020)
- [Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data](https://mlsanity.com/paper/2207.02384/) (2022)
- [BoXHED2.0: Scalable boosting of dynamic survival analysis](https://mlsanity.com/paper/2103.12591/) (2021)
- [Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards](https://mlsanity.com/paper/2110.08949/) (2021)
- [Ensemble methods for survival function estimation with time-varying covariates](https://mlsanity.com/paper/2006.00567/) (2020)
- [Random survival forests with multivariate longitudinal endogenous covariates](https://mlsanity.com/paper/2208.05801/) (2022)
- [MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks](https://mlsanity.com/paper/1811.10746/) (2018)
- [Boosted nonparametric hazards with time-dependent covariates](https://mlsanity.com/paper/1701.07926/) (2017)
- [Dynamic estimation with random forests for discrete-time survival data](https://mlsanity.com/paper/2103.01355/) (2021)
- [Model-independent variable selection via the rule-based variable priority](https://mlsanity.com/paper/2409.09003/) (2024)

## Abstract (arXiv)

Clinical data sources such as electronic health records and wearable sensors record patient status repeatedly over follow-up, often at irregular times and on different schedules for different measurements. These data create opportunities for continuously updated, individualized risk prediction. Existing approaches, however, often simplify the temporal structure for model fitting. We introduce Random Hazard Forests (RHF), a survival tree ensemble that estimates how a patient's hazard changes in continuous time as new measurements become available. The method formulates the estimation problem directly through a nonparametric hazard likelihood for predictable covariate processes. An efficient working model guides tree construction, after which flexible time-varying hazards are estimated for each terminal node. Given any predictable covariate path, each tree follows the path through its terminal nodes over time and assembles the corresponding node-level hazards into a trajectory. Averaging these trajectories across trees yields the pathwise hazard estimate. Because routing at each time uses only the covariate state available immediately beforehand, the construction accommodates internal longitudinal covariates without lookahead. Simulations and an intensive care application show that the forest accurately estimates changing risk under irregular and asynchronous covariate updates.