基于连续时间Hawkes过程的脓毒症相关异常的Granger因果链发现
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
AI总结：
本文提出一种结合ReLU链接函数的线性多变量Hawkes过程模型，通过两阶段梯度法恢复包含激发和抑制效应的Granger因果图，成功在Grady医院脓毒症患者数据中识别出具有高度可解释性的事件前驱因果链。
摘要：
现代医疗系统正越来越频繁地对电子病历（EMR）进行持续自动化监测以识别不良事件；然而，诸如脓毒症等许多事件尚未阐明可用于在其病程早期识别并拦截不良事件的前驱症状（即事件链）。具有临床相关性和可解释性的结果需要一个框架，该框架能够（i）推断EMR数据中发现的多项患者特征（如Labs、生命体征等）之间的时间相互作用，以及（ii）识别即将发生的不良事件（如脓毒症）之前且特有于该事件的模式。在这项工作中，我们提出了一种线性多变量Hawkes过程模型，结合ReLU链接函数，以恢复包含激发和抑制效应的Granger因果（GC）图。我们开发了一种可扩展的基于两阶段梯度的方法来获取最大替代似然估计量，并通过广泛的数值模拟证明了其有效性。我们的方法随后被扩展应用于美国佐治亚州亚特兰大市Grady医院系统收治的患者数据集，其中估计的GC图识别出了几条在脓毒症之前发生且具有高度可解释性的GC链。代码可在https://github.com/SongWei-GT/two-phase-MHP获取。
英文摘要：
Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i.e., event chains) that can be used to identify and intercept the adverse event early in its course. Clinically relevant and interpretable results require a framework that can (i) infer temporal interactions across multiple patient features found in EMR data (e.g., Labs, vital signs, etc.) and (ii) identify patterns that precede and are specific to an impending adverse event (e.g., sepsis). In this work, we propose a linear multivariate Hawkes process model, coupled with ReLU link function, to recover a Granger Causal (GC) graph with both exciting and inhibiting effects. We develop a scalable two-phase gradient-based method to obtain a maximum surrogate-likelihood estimator, which is shown to be effective via extensive numerical simulation. Our method is subsequently extended to a data set of patients admitted to Grady hospital system in Atlanta, GA, USA, where the estimated GC graph identifies several highly interpretable GC chains that precede sepsis. The code is available at \url{https://github.com/SongWei-GT/two-phase-MHP}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
