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：重症监护病房（ICU）患者中隐匿性出血（即内出血）的检测对重症监护医护人员构成重大挑战。由于失血可能无法通过临床症状明确显现，临床医生依赖于监测生命体征中提示出血事件的特定趋势。诊断此类事件固有的困难可能导致临床医生干预延迟，从而造成灾难性后果。因此，建立早期检测出血的方法具有广泛实用价值。我们开发了一种贝叶斯体制切换模型（RSM），该模型分析患者生命体征与实验室检查的变化趋势，从而对患者在任何时刻所处的潜在生理状态提供概率评估。本文基于我们从梅奥诊所整理的包含33,924例真实ICU患者住院记录的综合数据集展开研究。纵向应答测量值被建模为以所有截至当前时间点的潜在状态为条件的向量自回归过程，且潜在状态遵循马尔可夫过程。我们提出了一种新颖的贝叶斯采样程序来学习潜在生理状态的后验概率分布，并开发了一种方法以解释ICU入院前的生理变化。仿真实验与真实案例研究验证了我们方法的有效性。