论文标题
Granger因果链发现通过连续的鹰队过程，用于与败血症相关的扰动
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
论文作者
论文摘要
现代医疗保健系统正在进行对电子病历（EMR）的持续自动监视，以识别频率越来越多的不良事件；但是，许多败血症等事件都没有阐明的前序（即事件链），可用于识别和拦截不良事件的早期。临床上相关和可解释的结果需要一个框架，可以（i）在EMR数据中发现的多个患者特征（例如，实验室，生命体征等）中推断时间相互作用，以及（ii）确定在即将发生的不良事件（例如，seppsis）之前的模式。在这项工作中，我们提出了一个线性多元鹰队过程模型，再加上Relu Link函数，以恢复具有令人兴奋和抑制效果的Granger Causal（GC）图。我们开发了一种可扩展的基于两相梯度的方法，以获得最大的替代可能性估计量，该估计量通过广泛的数值模拟被证明是有效的。随后，我们的方法扩展到了美国佐治亚州亚特兰大市Grady医院系统的患者数据集，估计的GC图识别出败血症之前的几个高度可解释的GC链。该代码可在\ url {https://github.com/songwei-gt/two-phase-mhp}中获得。
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}.
