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Spontaneous Coronary Artery Dissection: Current State of the Science: A Scientific Statement From the American Heart Association Predicting Major Adverse Events in Patients With Acute Myocardial Infarction Universal Definition of Myocardial Infarction Association Between Haptoglobin Phenotype and Microvascular Obstruction in Patients With STEMI: A Cardiac Magnetic Resonance Study Effects of Aspirin for Primary Prevention in Persons with Diabetes Mellitus Complete Revascularization with Multivessel PCI for Myocardial Infarction Association Between Living in Food Deserts and Cardiovascular Risk Management of Percutaneous Coronary Intervention Complications: Algorithms From the 2018 and 2019 Seattle Percutaneous Coronary Intervention Complications Conference COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-up Hemodynamic Response to Nitroprusside in Patients With Low-Gradient Severe Aortic Stenosis and Preserved Ejection Fraction
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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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