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

J Nurs Scholarsh. 2021 May;53(3):278-287. doi: 10.1111/jnu.12637. Epub 2021 Feb 22.


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.

Keywords: 30-day readmission; Risk Prediction Model; electronic health records; machine learning; venous thromboembolism.

Publication types

  • Observational Study

MeSH terms

  • Academic Medical Centers
  • Adult
  • Aged
  • Electronic Health Records
  • Female
  • Humans
  • Machine Learning*
  • Male
  • Middle Aged
  • Models, Statistical*
  • Patient Readmission / statistics & numerical data*
  • Retrospective Studies
  • Risk Assessment / methods
  • Venous Thromboembolism / therapy*