Detection of occult hemorrhage (i.e., internal bleeding) in patients in intensive care units (ICUs) can pose significant challenges for critical care workers. Because blood loss may not always be clinically apparent, clinicians rely on monitoring vital signs for specific trends indicative of a hemorrhage event. The inherent difficulties of diagnosing such an event can lead to late intervention by clinicians which has catastrophic consequences. Therefore, a methodology for early detection of hemorrhage has wide utility. We develop a Bayesian regime switching model (RSM) that analyzes trends in patients' vitals and labs to provide a probabilistic assessment of the underlying physiological state that a patient is in at any given time. This article is motivated by a comprehensive dataset we curated from Mayo Clinic of 33,924 real ICU patient encounters. Longitudinal response measurements are modeled as a vector autoregressive process conditional on all latent states up to the current time point, and the latent states follow a Markov process. We present a novel Bayesian sampling routine to learn the posterior probability distribution of the latent physiological states, as well as develop an approach to account for pre-ICU-admission physiological changes. A simulation and real case study illustrate the effectiveness of our approach.

翻译：隐匿性出血（即内出血）的检测对重症监护病房（ICU）患者的医疗工作者构成重大挑战。由于失血在临床上可能并不明显，临床医生需依赖监测生命体征中提示出血事件的特定趋势。诊断此类事件的固有困难可能导致临床医生干预延迟，从而引发灾难性后果。因此，实现出血早期检测的方法具有广泛的应用价值。我们开发了一种贝叶斯状态切换模型（RSM），该模型分析患者生命体征与实验室检测指标的动态趋势，为患者在任何时刻所处的潜在生理状态提供概率性评估。本文依托于我们从梅奥诊所整理的综合数据集（涵盖33,924例真实ICU患者病例）。纵向响应测量值被建模为基于当前时间点前所有潜在状态条件的向量自回归过程，而潜在状态则遵循马尔可夫过程。我们提出一种新颖的贝叶斯采样程序，用于学习潜在生理状态的后验概率分布，并建立一种方法来解释患者入ICU前的生理变化。仿真实验与真实案例研究验证了我们方法的有效性。