RHF模型实时更新患者风险，适配不规则医疗数据。
Random Hazard Forests
- 基于非参数似然构建生存树集成，直接建模连续时间风险变化。
- 在重症监护数据中，对异步更新的变量实现高精度风险预测。
- 适合处理不规则、多源医疗数据，可动态追踪个体风险轨迹。
电子健康记录和可穿戴设备等临床数据以不规则时间间隔持续采集患者状态，为个性化实时风险预测提供可能。现有方法常简化时间结构后再建模。本文提出随机危险森林（Random Hazard Forests, RHF），一种生存树集成模型，能学习患者随新数据到来而连续变化的危险率。RHF通过可预测协变量过程的非参数危险率似然直接建模估计问题，利用高效工作模型指导树的构建，再对每个终端节点估计灵活的时间变化危险率。对于任意可预测的协变量路径，每棵树沿路径经过各终端节点，并将节点级危险率组合成风险轨迹。通过平均多棵树的轨迹，得到路径级危险率估计。由于每个时间点的路径选择仅依赖前一刻可用的协变量状态，RHF无需前瞻信息，可自然处理内部纵向协变量。模拟与重症监护应用均表明，该模型在异步、不规则协变量更新下仍能准确估计动态风险。
原文摘要 · Abstract (English)
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.
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