IEEE J Biomed Health Inform. 2014 Sep;18(5):1560-70. doi: 10.1109/JBHI.2013.2294692.
Late onset neonatal sepsis is one of the major clinical concerns when premature babies receive intensive care. Current practice relies on slow laboratory testing of blood cultures for diagnosis. A valuable research question is whether sepsis can be reliably detected before the blood sample is taken. This paper investigates the extent to which physiological events observed in the patient's monitoring traces could be used for the early detection of neonatal sepsis. We model the distribution of these events with an autoregressive hidden Markov model (AR-HMM). Both learning and inference carefully use domain knowledge to extract the baby's true physiology from the monitoring data. Our model can produce real-time predictions about the onset of the infection and also handles missing data. We evaluate the effectiveness of the AR-HMM for sepsis detection on a dataset collected from the Neonatal Intensive Care Unit at the Royal Infirmary of Edinburgh.
晚发型新生儿败血症是早产儿接受重症监护时的主要临床关注点之一。目前的做法依赖于对血培养的缓慢实验室检测来进行诊断。一个有价值的研究问题是，是否可以在采集血样之前可靠地检测到败血症。本文研究了在采集血样之前，可以在多大程度上利用患者监测轨迹中观察到的生理事件来早期检测新生儿败血症。我们使用自回归隐马尔可夫模型 (AR-HMM) 对这些事件的分布进行建模。学习和推断过程都仔细地利用领域知识从监测数据中提取婴儿的真实生理状态。我们的模型可以实时预测感染的发生，并且还可以处理缺失数据。我们在爱丁堡皇家医院新生儿重症监护病房收集的数据集上评估了 AR-HMM 对败血症检测的有效性。
