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High-Sensitivity Troponin I Levels and Coronary Artery Disease Severity, Progression, and Long-Term Outcomes Implications of Alternative Definitions of Peri-Procedural Myocardial Infarction After Coronary Revascularization Coronary Artery Disease in Patients With Out-of-Hospital Refractory Ventricular Fibrillation Cardiac Arrest Timing of Oral P2Y12 Inhibitor Administration in Patients With Non-ST-Segment Elevation Acute Coronary Syndrome Mild Hypothermia in Cardiogenic Shock Complicating Myocardial Infarction - The Randomized SHOCK-COOL Trial Optimum Blood Pressure in Patients With Shock After Acute Myocardial Infarction and Cardiac Arrest Impact of tissue protrusion after coronary stenting in patients with ST-segment elevation myocardial infarction Prevalence of anginal symptoms and myocardial ischemia and their effect on clinical outcomes in outpatients with stable coronary artery disease: data from the International Observational CLARIFY Registry Prognostic Value of SYNTAX Score in Patients With Infarct-Related Cardiogenic Shock: Insights From the CULPRIT-SHOCK Trial Classic crush and DK crush stenting techniques
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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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