谵妄
接收机工作特性
医学
围手术期
心脏外科
支持向量机
机器学习
随机森林
人工智能
内科学
外科
重症监护医学
计算机科学
作者
Tan Yang,Hai Yang,Yan Liu,Xiao Liu,Yijie Ding,Run Li,An-Qiong Mao,Yue Huang,Xiaoliang Li,Ying Zhang,Fengxu Yu
标识
DOI:10.1016/j.compbiomed.2023.107818
摘要
Postoperative delirium (POD) is a common postoperative complication in elderly patients, especially those undergoing cardiac surgery, which seriously affects the short- and long-term prognosis of patients. Early identification of risk factors for the development of POD can help improve the perioperative management of surgical patients. In the present study, five machine learning models were developed to predict patients at high risk of delirium after cardiac surgery and their performance was compared. A total of 367 patients who underwent cardiac surgery were retrospectively included in this study. Using single-factor analysis, 21 risk factors for POD were selected for inclusion in machine learning. The dataset was divided using 10-fold cross-validation for model training and testing. Five machine learning models (random forest (RF), support vector machine (SVM), radial based kernel neural network (RBFNN), K-nearest neighbour (KNN), and Kernel ridge regression (KRR)) were compared using area under the receiver operating characteristic curve (AUC‐ROC), accuracy (ACC), sensitivity (SN), specificity (SPE), and Matthews coefficient (MCC). Among 367 patients, 105 patients developed POD, the incidence of delirium was 28.6 %. Among the five ML models, RF had the best performance in ACC (87.99 %), SN (69.27 %), SPE (95.38 %), MCC (70.00 %) and AUC (0.9202), which was far superior to the other four models. Delirium is common in patients after cardiac surgery. This analysis confirms the importance of the computational ML models in predicting the occurrence of delirium after cardiac surgery, especially the outstanding performance of the RF model, which has practical clinical applications for early identification of patients at risk of developing POD.
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