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The association between body mass index and obesity with survival in pulmonary arterial hypertension Chronic total occlusion intervention of the non-infarct-related artery in acute myocardial infarction patients: the Korean multicenter chronic total occlusion registry Circulating MicroRNAs and Monocyte-Platelet Aggregate Formation in Acute Coronary Syndrome Inflammatory Bowel Disease and Acute Coronary Syndromes: From Pathogenesis to the Fine Line Between Bleeding and Ischemic Risk Association Between Collateral Circulation and Myocardial Viability Evaluated by Cardiac Magnetic Resonance Imaging in Patients With Coronary Artery Chronic Total Occlusion Epinephrine Versus Norepinephrine for Cardiogenic Shock After Acute Myocardial Infarction Early versus delayed invasive intervention in acute coronary syndromes Use of Mechanical Circulatory Support Devices Among Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock Risk Factors Associated With Major Cardiovascular Events 1 Year After Acute Myocardial Infarction Association of Silent Myocardial Infarction and Sudden Cardiac Death

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.