Characterization of cardiac resynchronization therapy response through machine learning and personalized models.
Journal:
Computers in biology and medicine
PMID:
39142225
Abstract
INTRODUCTION: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response.