Personalized Risk Prediction for 30‐Day Readmissions With Venous Thromboembolism Using Machine Learning. Issue 3 (22nd February 2021)

• Record Type: Journal Article
• Authors: Park, Jung In; Kim, Doyub; Lee, Jung‐Ah; Zheng, Kai; Amin, Alpesh
• Is Part Of: Journal of nursing scholarship. Volume 53:Issue 3(2021)
• Page Start: 278
• Page End: 287
• Publication Date: 2021-02-22
• Subjects: 30‐day readmission -- electronic health records -- machine learning -- Risk Prediction Model -- venous thromboembolism; Nursing -- Periodicals; Nursing -- United States -- Periodicals; 610.73
• DOI: 10.1111/jnu.12637
• Languages: English
• ISSNs: 1527-6546

Abstract

Abstract: 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.