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Percutaneous Support Devices for Percutaneous Coronary Intervention Management of Patients With NSTE-ACS: A Comparison of the Recent AHA/ACC and ESC Guidelines Coronary flow velocity reserve predicts adverse prognosis in women with angina and noobstructive coronary artery disease: resultsfrom the iPOWER study 稳定性冠心病诊断与治疗指南 Left Ventricular Assist Devices for Lifelong Support 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines Cardiovascular Biomarkers and Imaging in Older Adults: JACC Council Perspectives Guiding Principles for Chronic Total Occlusion Percutaneous Coronary Intervention Statin Safety and Associated Adverse Events: A Scientific Statement From the American Heart Association 2019 ESC Guidelines for the management of patients with supraventricular tachycardia The Task Force for the management of patients with supraventricular tachycardia of the European Society of Cardiology (ESC): Developed in collaboration with the Association for European Paediatric and Congenital Cardiology (AEPC)he management of patients with)
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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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