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Type 2 Diabetes Mellitus and Heart Failure: A Scientific Statement From the American Heart Association and the Heart Failure Society of America Natriuretic Peptide-Guided Heart Failure Therapy After the GUIDE-IT Study Coronary plaque redistribution after stent implantation is determined by lipid composition: A NIRS-IVUS analysis Short- versus long-term duration of dual-antiplatelet therapy after coronary stenting: a randomized multicenter trial Association of Abnormal Left Ventricular Functional Reserve With Outcome in Heart Failure With Preserved Ejection Fraction Modifiable lifestyle factors and heart failure: A Mendelian randomization study Combined Tricuspid and Mitral Versus Isolated Mitral Valve Repair for Severe MR and TR: An Analysis From the TriValve and TRAMI Registries Metabolic Interactions and Differences between Coronary Heart Disease and Diabetes Mellitus: A Pilot Study on Biomarker Determination and Pathogenesis Baseline Features of the VICTORIA (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction) Trial Catastrophic catheter-induced coronary artery vasospasm successfully rescued using intravascular ultrasound imaging guidance

Review Article2020 Jul 16;229:1-17.

JOURNAL:Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.