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Random Hazard Forests presents a novel machine learning framework for survival analysis, specifically addressing the challenge of modeling patient risk in continuous time using irregularly sampled clinical data. By constructing an ensemble of survival trees that adapt to asynchronous covariate updates without lookahead, this method offers a more flexible alternative to existing approaches that often simplify temporal structures. The technique demonstrates improved accuracy in estimating dynamic hazards for individualized risk prediction in intensive care settings.
Random Hazard Forests
arXiv:2608.21597v1 Announce Type: cross Abstract: 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 before modeling it. We introduce Random Hazard Forests (RHF), a survival tree ensemble that learns how a patient's hazard changes in continuous time as new measurements become available. RHF 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 RHF pathwise hazard estimate. Because routing at each time uses only the covariate state available immediately beforehand, RHF accommodates internal longitudinal covariates without lookahead. Simulations and an intensive care application show that RHF accurately estimates changing risk under irregular and asynchronous covariate updates.
Abstract: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 before modeling it. We introduce Random Hazard Forests (RHF), a survival tree ensemble that learns how a patient's hazard changes in continuous time as new measurements become available. RHF 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 RHF pathwise hazard estimate. Because routing at each time uses only the covariate state available immediately beforehand, RHF accommodates internal longitudinal covariates without lookahead. Simulations and an intensive care application show that RHF accurately estimates changing risk under irregular and asynchronous covariate updates.
Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2608.21597 [stat.ML]
(or arXiv:2608.21597v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2608.21597 Focus to learn more arXiv-issued DOI via DataCite
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