Chapfuwa Paidamoyo, Tao Chenyang, Li Chunyuan, Page Courtney, Goldstein Benjamin, Carin Lawrence, Henao Ricardo
Duke University.
Proc Mach Learn Res. 2018 Jul;80:735-744.
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. We present a deep-network-based approach that leverages adversarial learning to address a key challenge in modern time-to-event modeling: nonparametric estimation of event-time distributions. We also introduce a principled cost function to exploit information from censored events (events that occur subsequent to the observation window). Unlike most time-to-event models, we focus on the estimation of time-to-event distributions, rather than time ordering. We validate our model on both benchmark and real datasets, demonstrating that the proposed formulation yields significant performance gains relative to a parametric alternative, which we also propose.
现代健康数据科学应用利用丰富的分子和电子健康数据，为机器学习构建统计模型以支持临床实践提供了机会。生存分析，也称为事件发生时间分析，是此类统计模型最具代表性的例子之一。我们提出了一种基于深度网络的方法，该方法利用对抗学习来解决现代事件发生时间建模中的一个关键挑战：事件时间分布的非参数估计。我们还引入了一个有原则的成本函数，以利用删失事件（在观察窗口之后发生的事件）中的信息。与大多数事件发生时间模型不同，我们专注于事件发生时间分布的估计，而不是时间顺序。我们在基准数据集和真实数据集上验证了我们的模型，证明所提出的公式相对于我们也提出的参数替代方案产生了显著的性能提升。
