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Extended antiplatelet therapy with clopidogrel alone versus clopidogrel plus aspirin after completion of 9- to 12-month dual antiplatelet therapy for acute coronary syndrome patients with both high bleeding and ischemic risk. Rationale and design of the OPT-BIRISK double-blinded, placebo-controlled randomized trial Randomized Evaluation of TriGuard 3 Cerebral Embolic Protection After Transcatheter Aortic Valve Replacement: REFLECT II Ambulatory Electrocardiogram Monitoring in Patients Undergoing Transcatheter Aortic Valve Replacement: JACC State-of-the-Art Review 5-Year Outcomes Comparing Surgical Versus Transcatheter Aortic Valve Replacement in Patients With Chronic Kidney Disease Stroke Rates Following Surgical Versus Percutaneous Coronary Revascularization Individualized antiplatelet therapy after drug-eluting stent deployment: Implication of clinical trials of different durations of dual antiplatelet therapy Assessment of Vascular Dysfunction in Patients Without Obstructive Coronary Artery Disease: Why, How, and When Evolving concepts in the management of antithrombotic therapy in patients undergoing transcatheter aortic valve implantation Association of Effective Regurgitation Orifice Area to Left Ventricular End-Diastolic Volume Ratio With Transcatheter Mitral Valve Repair OutcomesA Secondary Analysis of the COAPT Trial Edoxaban-based versus vitamin K antagonist-based antithrombotic regimen after successful coronary stenting in patients with atrial fibrillation (ENTRUST-AF PCI): a randomised, open-label, phase 3b trial
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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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