Abstract：
Arterial hypotension is associated with incidence of postoperative complications, such as myocardial infarction or acute kidney injury. Little research has been conducted for the real-time prediction of hypotension, even though many studies have been performed to investigate the factors which affect hypotension events. This forecasting problem is quite challenging compared to diagnosis that detects high-risk patients at current. The forecasting problem that specifies when events occur is more challenging than the forecasting problem that does not specify the event time. In this work, we challenge the forecasting problem in 5 min advance. For that, we aim to build a systematic feature engineering method that is applicable regardless of vital sign species, as well as a machine learning model based on these features for real-time predictions 5 min before hypotension. The proposed feature extraction model includes statistical analysis, peak analysis, change analysis, and frequency analysis. After applying feature engineering on invasive blood pressure (IBP), we build a random forest model to differentiate a hypotension event from other normal samples. Our model yields an accuracy of 0.974, a precision of 0.904, and a recall of 0.511 for predicting hypotensive events.
摘要：
动脉低血压与术后并发症的发生率有关。如心肌梗死或急性肾损伤。很少有研究对低血压的实时预测，尽管已经进行了许多研究来调查影响低血压事件的因素。与当前检测高风险患者的诊断相比，此预测问题非常具有挑战性。指定事件发生时间的预测问题比不指定事件时间的预测问题更具挑战性。在这项工作中,我们提前5分钟挑战预测问题。为此，我们的目标是建立一个系统的特征工程方法，无论生命体征物种如何都适用，以及基于这些功能的机器学习模型，用于在低血压前5分钟进行实时预测。提出的特征提取模型包括统计分析，峰值分析,变化分析,和频率分析。在对有创血压（IBP）应用特征工程后，我们建立了一个随机森林模型来区分低血压事件和其他正常样本.我们的模型预测低血压事件的准确性为0.974，精度为0.904，召回率为0.511。
