MACHINE LEARNING APPLICATIONS IN MORTALITY PREDICTION FOR ELDERLY PATIENTS WITH HEART FAILURE: CURRENT EVIDENCE AND PERSPECTIVES

Abstract

Heart failure in older adults is associated with high rates of complications, hospital readmissions, and mortality, yet clinical evidence specifically tailored to this population remains limited. Recently, artificial intelligence and machine learning have been applied to mortality prediction in patients with heart failure, demonstrating notable advantages over traditional prognostic models. This review focuses on recent studies investigating the use of machine learning in predicting mortality among older adults with heart failure, compares their performance with established clinical risk scores, and discusses the potential for clinical application to advance personalized care and optimize therapeutic strategies.

Keywords: heart failure; frailty; older adults; artificial intelligence; machine learning