Personalized Risk Prediction for 30‐Day Readmissions With Venous Thromboembolism Using Machine Learning
Jung In Park, Doyub Kim, Jung‐Ah Lee, Kai Wen Zheng, Alpesh Amin · Journal of Nursing Scholarship · 2021
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.