Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery - Xue, Qiong; Wen, Duan; Ji, Mu-Huo; Tong, Jianhua; Yang, Jian-Jun; Zhou, Cheng-Mao (Frontiers in Medicine, 2021)
Objective: Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis Methods: This is a secondary data analysis study. We use 5 machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc and gbm) to predict postoperative pulmonary complications. Results: 926 cases were included in this study; 187 cases (20.19%) had PPCs. The five most important variables for the postoperative weight were preoperative albumin, cholesterol on the 3rd day after surgery, albumin on the day of surgery, platelet count on the first day after surgery and cholesterol count on the first day after surgery for pulmonary complications. In the test group: the logistic regression model shows AUC = 0.808, accuracy = 0.824 and precision = 0.621; Decision tree shows AUC = 0.702, accuracy = 0.795 and precision = 0.486; The GradientBoosting model shows AUC = 0.788, accuracy = 0.827 and precision = 1.000; The Xgbc model shows AUC = 0.784, accuracy = 0.806 and precision = 0.583. The Gbm model shows AUC = 0.814, accuracy = 0.806 and precision = 0.750. Conclusion: Machine learning algorithms can predict patients’ PPCs with acute diffuse peritonitis. Moreover, the results of the importance matrix for the Gbdt algorithm model show that albumin, cholesterol, age and platelets are the main variables that account for the highest pulmonary complication weights.