Bigdata Engineering Department, SCH Media Labs, Soonchunhyang University, Asan 31538, Korea.
Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea.
Sensors (Basel). 2022 Apr 19;22(9):3108. doi: 10.3390/s22093108.
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.
低血压与术后并发症的发生有关，如心肌梗死或急性肾损伤。尽管已经有许多研究来探讨影响低血压事件的因素，但对于低血压的实时预测研究较少。与目前检测高危患者的诊断相比，这种预测问题极具挑战性。与不指定事件时间的预测问题相比，指定事件发生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，我们旨在建立一种系统的特征工程方法，该方法适用于与生命体征种类无关的情况，以及基于这些特征的机器学习模型，以便在低血压发生前 5 分钟进行实时预测。所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）进行特征工程处理后，我们构建了一个随机森林模型，以将低血压事件与其他正常样本区分开来。我们的模型在预测低血压事件时的准确率为 0.974，精度为 0.904，召回率为 0.511。
