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Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications Use of Risk Assessment Tools to Guide Decision-Making in the Primary Prevention of Atherosclerotic Cardiovascular Disease : A Special Report From the American Heart Association and American College of Cardiology Lipoprotein(a) in Alzheimer, Atherosclerotic, Cerebrovascular, Thrombotic, and Valvular Disease: Mendelian Randomization Investigation Efficacy and Safety of Ticagrelor Monotherapy in Patients Undergoing Multivessel PCI Rationale and design of the comparison between a P2Y12 inhibitor monotherapy versus dual antiplatelet therapy in patients undergoing implantation of coronary drug-eluting stents (SMART-CHOICE): A prospective multicenter randomized trial Sox17 Controls Emergence and Remodeling of Nestin-Expressing Coronary Vessels A Platelet Function Modulator of Thrombin Activation Is Causally Linked to Cardiovascular Disease and Affects PAR4 Receptor Signaling Rationale and design of a prospective substudy of clinical endpoint adjudication processes within an investigator-reported randomised controlled trial in patients with coronary artery disease: the GLOBAL LEADERS Adjudication Sub-StudY (GLASSY) Does Risk of Premature Discontinuation of Dual-Antiplatelet Therapy Following PCI Attenuate With Increasing Age? Rivaroxaban Plus Aspirin in Patients With Vascular Disease and Renal Dysfunction: From the COMPASS Trial

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.