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Acute myocardial infarction (AMI) is one of the most severe manifestation ofcoronary artery disease. ST-segment elevation myocardial infarction (STEMI) isthe most serious type of AMI. We proposed to develop a machine learningalgorithm based on the home page of electronic medical record (HPEMR) forpredicting in-hospital mortality of patients with STEMI in the early stage.Methods: This observational study applied clinical information collectedbetween 2013 and 2017 from 7 tertiary hospitals in Shenzhen, China. Thepatients' STEMI data were used to train 4 different machine learning algorithmsto predict in-hospital mortality among the patients with STEMI, includingLogistic Regression, Support Vector Machine, Gradient Boosting Decision Tree,and Artificial Neuron network. Results: A total of 5865 patients with STEMIwere enrolled in our study. The model was developed by considering 3 types ofvariables, which included demographic data, diagnosis and comorbidities, andhospitalization information basing on HPEMR. The association of selectedfeatures using univariant logistic regression was reported. Specially, for thecomorbidities, atrial fibrillation (OR: 11.0; 95% CI: 5.64 - 20.2), acute renalfailure (OR: 9.75; 95% CI: 3.81 - 25.0), type 2 diabetic nephropathy (OR: 5.45;95% CI: 1.57 - 19.0), acute heart failure (OR: 6.05; 95% CI: 1.99 - 14.9), andcardiac function grade IV (OR: 28.6; 95% CI: 20.6 - 39.6) were found to beassociated with a high odds of death. Within the test dataset, our model showeda good discrimination ability as measured by area under the receiver operatingcharacteristic curve (AUC; 0.879) (95% CI: 0.825 - 0.933).

Machine Learning Algorithms for Predicting in-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infar