论文标题
BOXHED：带有动态协变量的增强确切危险估计器
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
论文作者
论文摘要
医疗监测设备的扩散使得可以在高频中跟踪健康生命力，从而能够发展动态健康风险评分，而随着基础读数的变化。生存分析，特别是危害估计，非常适合分析此数据流以预测疾病发作是随时间变化的生命力的函数。本文介绍了通过梯度提升来估算危险函数的Boxhed软件包（发音为“ Box-Head”）。 Boxhed 1.0是在Lee，Chen，Ishwaran（2017）中提出的基于树的新型实现，该实现旨在以完全非参数方式处理时间依赖的协变量。 Boxhed也是Lee，Chen，Ishwaran（2017）的首个公开软件实施。从弗雷明汉心脏研究中应用箱子对心血管疾病的发作数据揭示了已知危险因素之间的新型相互作用效应，并有可能解决临床文献中的一个开放问题。
The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with the underlying readings. Survival analysis, in particular hazard estimation, is well-suited to analyzing this stream of data to predict disease onset as a function of the time-varying vitals. This paper introduces the software package BoXHED (pronounced 'box-head') for nonparametrically estimating hazard functions via gradient boosting. BoXHED 1.0 is a novel tree-based implementation of the generic estimator proposed in Lee, Chen, Ishwaran (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner. BoXHED is also the first publicly available software implementation for Lee, Chen, Ishwaran (2017). Applying BoXHED to cardiovascular disease onset data from the Framingham Heart Study reveals novel interaction effects among known risk factors, potentially resolving an open question in clinical literature.
