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. Currently, there does not exist reliable framework for discovering or describing causal chains that precede adverse hospital events. Clinically relevant and interpretable results require a framework that can (1) infer temporal interactions across multiple patient features found in EMR data (e.g., labs, vital signs, etc.) and (2) can 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 maximize a surrogate-likelihood and estimate the problem parameters, which is shown to be effective via extensive numerical simulation. Our method is subsequently extended to a data set of patients admitted to an academic level 1 trauma center located in Atalanta, GA, where the estimated GC graph identifies several highly interpretable chains that precede sepsis. Here, we demonstrate the effectiveness of our approach in learning a GC graph over Sepsis Associated Derangements (SADs), but it can be generalized to other applications with similar requirements.

翻译：现代医疗系统正在对电子医疗记录进行连续、自动的监视,以越来越频繁地识别不良事件;然而,许多事件,如败血症等,没有说明可用于早期识别和拦截不利事件的预兆(即事件链 ) 。目前,还没有可靠的框架来发现或描述医院不利事件之前的因果关系。临床相关和可解释的结果要求有一个框架,这一框架可以(1) 推断电子医疗记录数据(如实验室、生命迹象等)中多个患者特征之间的时间互动;以及(2) 能够查明预兆不利事件(如败血症)之前和特有的模式。在此工作中,我们建议建立一个线性多变形鹰进程模型,加上累死鹰联系功能,以恢复Granger Causal(GC)图,该图既具有刺激作用,又具有抑制作用。我们开发了一种可缩放两阶段的梯度基于生态系统的方法,以最大限度地实现可探测性,并估计出问题参数,通过广泛的数字模拟显示其前期(如Sepsial ) 。我们的方法随后将一个直径的GAL-DRevorateal 解释了我们GAS程中的一些数据集。