Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information.

ABSTRACT:

Background

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia associated with morbidity and substantial healthcare costs. While patients with cardiovascular disease experience the greatest risk of new-onset AF, no risk model has been developed to predict AF occurrence in this population. We hypothesized that a patient-specific model could be delivered using cardiovascular magnetic resonance (CMR) disease phenotyping, contextual patient health information, and machine learning.

Methods

Nine thousand four hundred forty-eight patients referred for CMR imaging were enrolled and followed over a 5-year period. Seven thousand, six hundred thirty-nine had no prior history of AF and were eligible to train and validate machine learning algorithms. Random survival fo

SUBMITTER: Dykstra S 

PROVIDER: S-EPMC9554748 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature