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2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA /ASH/ ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary : A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines Thin Composite-Wire-Strut Zotarolimus-Eluting Stents Versus Ultrathin-Strut Sirolimus-Eluting Stents in BIONYX at 2 Years Complete Revascularization Versus Culprit Lesion Only in Patients With ST-Segment Elevation Myocardial Infarction and Multivessel Disease: A DANAMI-3-PRIMULTI Cardiac Magnetic Resonance Substudy Single-Molecule hsTnI and Short-Term Risk in Stable Patients With Chest Pain Cardiac monocytes and macrophages after myocardial infarction Routine Continuous Electrocardiographic Monitoring Following Percutaneous Coronary Interventions Relationship of C-reactive protein reduction to cardiovascular event reduction following treatment with canakinumab: a secondary analysis from the CANTOS randomised controlled trial Randomized comparison of stent strut coverage following angiography- or optical coherence tomography-guided percutaneous coronary intervention Long-Term Follow-Up of Complete Versus Lesion-Only Revascularization in STEMI and Multivessel Disease: The CvLPRIT Trial Routinely reported ejection fraction and mortality in clinical practice: where does the nadir of risk lie?
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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.

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