The American journal of medicine
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Randomized Controlled Trial
Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin Versus Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial.
Atrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to implement appropriate monitoring strategy and timely treatment, reducing the impact of atrial fibrillation on patients' health. ⋯ The use of machine learning can prove useful in identifying novel cardiac risk factors. Our analysis has shown that "social factors," such as living alone, may disproportionately increase the risk of atrial fibrillation in the under-represented non-White patient group with heart failure, highlighting the need for more studies focusing on stratification of multiracial cohorts to better uncover the heterogeneity of atrial fibrillation.