Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.

ABSTRACT: Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. The development and independent validation datasets included 62,154 patients from two EDs and 73,453 patients from one ED, respectively. Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. These machine learning models were also compared against an existing

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11432862 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature