Machine learning predictions of unplanned readmissions using electronic medical records: Predictor importance across medical and surgical patient populations.

ABSTRACT: Hospital readmissions prolong patient suffering and increase healthcare expenditures. While several studies have attempted to develop prediction models to reduce readmissions, most have demonstrated modest predictive accuracy. To improve upon prior approaches, we conducted an overview of systematic reviews to identify the most relevant predictor variables, then subsequently developed machine learning models in a retrospective, multisite study across eight hospitals. The patient sample comprised 200,799 inpatient stays from eligible hospitalizations, based on the Centers for Medicare and Medicaid Services (CMS) definition of unplanned readmissions within 30 days of discharge. We constructed random forest models and evaluated out-of-sample performance using the area under the receiver operat

SUBMITTER: Havranek MM 

PROVIDER: S-EPMC12410744 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature