Chan Brandon, Sedghi Alireza, Laird Philip, Maslove David, Mousavi Parvin
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:566-569. doi: 10.1109/EMBC.2019.8856985.
Forecasting acute hypotensive episodes (AHE) in intensive care patients has been of recent interest to researchers in the healthcare domain. Advance warning of an impending AHE may give care providers additional information to help mitigate the negative clinical impact of a serious event such as an AHE or prompt a search for an evolving disease process. However, the currently accepted definition of AHE is restrictive does not account for inter-patient variability. In this paper, we propose a novel definition of an AHE based on patient-specific features of blood pressure recordings. Next, we utilize a deep learning-based method to predict the onset of an AHE from multiple physiological readings for different definitions of the prediction task including variable input and gap lengths. Using a cohort of 538 patients, our model was able to successfully predict the onset of an AHE with an accuracy and AUC score of 0.80 and 0.87 respectively. Compared to a baseline logistic regression model, our model outperforms the baseline in most of the definitions of the prediction task.
预测重症监护患者的急性低血压发作（AHE）最近引起了医疗保健领域研究人员的关注。即将发生AHE的提前预警可能会为护理人员提供更多信息，以帮助减轻诸如AHE等严重事件的负面临床影响，或促使寻找正在发展的疾病过程。然而，目前公认的AHE定义具有局限性，没有考虑到患者之间的差异。在本文中，我们基于血压记录的患者特定特征提出了一种新的AHE定义。接下来，我们使用一种基于深度学习的方法，根据不同预测任务定义（包括可变输入和间隔长度）的多个生理读数来预测AHE的发作。使用一个由538名患者组成的队列，我们的模型能够成功预测AHE的发作，准确率和AUC分数分别为0.80和0.87。与基线逻辑回归模型相比，我们的模型在大多数预测任务定义中都优于基线。
