A machine learning-based risk prediction model for atrial fibrillation in critically ill patients.

ABSTRACT:

Background

Atrial fibrillation (AF) in critically ill patients increases morbidity, hospital stays, and costs. Existing prediction tools are limited in intensive care unit (ICU) settings.

Objective

This study developed a machine learning-based model to enable early AF risk identification and prevention.

Methods

In this retrospective cohort study, adult patients admitted to the ICU were identified from the MIMIC-IV (Medical Information Mart for Intensive Care-IV) database, including 47 clinical and laboratory variables. The primary outcome was AF within the first 48 hours of admission. Multiple machine learning models were trained to predict AF, with the top-performing model undergoing hyperparameter tuning. A compact model was developed using 15 variables and 2 novel

SUBMITTER: Alomari L 

PROVIDER: S-EPMC12147569 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature