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 clearly 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 a 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 pattern(s) which 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 $g(x) = x^+$ link function to allow potential inhibition effects, in order to recover a Granger Causal (GC) graph. We develop a two-phase gradient-based scheme to maximize a surrogate of likelihood to estimate the problem parameters. This two-phase algorithm is scalable and shown to be effective via our numerical simulation. It is subsequently extended to a data set of patients admitted to Grady hospital system in Atalanta, GA, where the fitted Granger Causal graph identifies several highly interpretable chains that precede sepsis.
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,没有清楚阐明可用于早期识别和拦截不利事件的预兆(即事件链),目前还没有一个可靠的框架来发现或描述医院不利事件之前的因果关系链。临床相关和可解释的结果需要一个框架,这一框架可以(1) 将电子医疗记录数据(如实验室、生命迹象等)中发现的许多病人特征之间的时间互动推移到跨多个病人特征之间;以及(2) 败血症等许多事件没有清楚地说明出在即将到来的不利事件之前和特有的模式(即事件链); 在这项工作中,我们提出了一个线性多变形霍克斯进程模型,加上$g(x)=x ⁇ 美元链接功能,以允许潜在的抑制效应,从而恢复Granger Causal(GC)图表。我们开发了两阶段梯度计划,以最大限度地推断估计问题参数的可能性。在两阶段里阶段里,通过高档的卡路里卡路里(GA)系统后显示一个可接受的高级卡路路段数据。