Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction - Zhou, Xingyu; Li, Xianying; Zhang, Zijun; Han, Qinrong; Deng, Huijiao; Jiang, Yi; Tang, Chunxiao; Yang, Lin (Frontiers in Physiology, 2022)
Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Data covering laboratory indicators, medication use, basic patient information, and patient scores on the Acute Physiology Score (APS)-III scale were obtained. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, naive Bayes, RUSBoost, KNN, and SVM). The accuracy of the patient prognostic risk-prediction model constructed using SVM was 92.2%, with an AUC value of 0.98, and its confidence interval was the narrowest with higher fitting accuracy and better performance compared to the remaining five models. In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.