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}.

翻译：现代医疗系统正在对电子病历（EMR）进行持续自动化监测，以更高频率识别不良事件。然而，许多事件（如脓毒症）尚未阐明可用于早期识别和干预的前驱症状（即事件链）。临床相关且可解释的结果需要一种框架，该框架能够：（i）推断EMR数据中多个患者特征（如实验室检查、生命体征等）之间的时间交互作用；（ii）识别不良事件（如脓毒症）发生前特有的模式。本研究提出一种结合ReLU链接函数的线性多元霍克斯过程模型，用于恢复同时包含兴奋和抑制效应的格兰杰因果图。我们开发了一种可扩展的两阶段梯度方法，以获取最大代理似然估计量，并通过广泛数值模拟验证其有效性。该方法随后被应用于美国佐治亚州亚特兰大格雷迪医院系统的患者数据集，估计得出的格兰杰因果图识别出多个高度可解释的、发生于脓毒症前的因果链。代码开源于\url{https://github.com/SongWei-GT/two-phase-MHP}。