Personalized Risk Prediction for 30‐Day Readmissions With Venous Thromboembolism Using Machine Learning

PURPOSE The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE). DESIGN This study was a retrospective, observational study. METHODS We extracted and preprocessed the structured electronic health records (EHRs) from a single academic hospital. Then we developed and evaluated three prediction models using logistic regression, the balanced random forest model, and the multilayer perceptron. RESULTS The study sample included 158,804 total admissions; VTE-positive cases accounted for 2,080 admissions from among 1,695 patients (1.31%). Based on the evaluation results, the balanced random forest model outperformed the other two risk prediction models. CONCLUSIONS This study delivered a high-performing, validated risk prediction tool using machine learning and EHRs to identify patients at high risk for VTE after discharge. CLINICAL RELEVANCE The risk prediction model developed in this study can potentially guide treatment decisions for discharged patients for better patient outcomes.