发表机构
Miller School of Medicine, University of Miami; Heart and Vascular Institute, Cleveland Clinic; Kogalur & Company, Inc.; Goizueta Business School（迈阿密大学米勒医学院; 克利夫兰诊所心脏与血管研究所; 科加卢尔公司; 戈伊祖塔商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出随机风险森林（RHF），一种生存树集成模型，可处理不规则异步协变量更新，准确估计患者时变风险，经模拟与重症监护应用验证有效。
AI 中文摘要
电子健康记录、可穿戴传感器等临床数据源会在随访期间多次记录患者状态，记录时间通常不规则，不同测量的安排也存在差异，这些数据为构建持续更新的个体化风险预测模型提供了机会。然而，现有方法在建模前往往会简化时间结构。我们提出了随机风险森林（Random Hazard Forests，RHF），这是一种生存树集成模型，能够在新测量值获取时学习患者风险随连续时间的变化规律。RHF通过针对可预测协变量过程的非参数风险似然直接构建估计问题，采用高效工作模型指导树的构建，之后为每个终端节点估计灵活的时变风险。对于任意可预测的协变量路径，每棵树会随时间沿路径到达其终端节点，并将对应节点级风险组合成轨迹，对所有树的这些轨迹取平均即可得到RHF的路径式风险估计。由于在每个时间点仅使用此前可立即获得的协变量状态进行路由，RHF可处理无前瞻的内部纵向协变量。模拟实验和重症监护应用表明，在协变量更新不规则且异步的情况下，RHF能准确估计变化的风险。
英文摘要
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.
