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Intravascular Ultrasound and Angioscopy Assessment of Coronary Plaque Components in Chronic Totally Occluded Lesions Prior Balloon Valvuloplasty Versus Direct Transcatheter Aortic Valve Replacement: Results From the DIRECTAVI Trial Six-month versus 12-month dual antiplatelet therapy after implantation of drug-eluting stents: the Efficacy of Xience/Promus Versus Cypher to Reduce Late Loss After Stenting (EXCELLENT) randomized, multicenter study Transcatheter versus Surgical Aortic Valve Replacement in Patients with Prior Cardiac Surgery in the Randomized PARTNER 2A Trial Predictors of high residual gradient after transcatheter aortic valve replacement in bicuspid aortic valve stenosis Novel predictors of late lumen enlargement in distal reference segments after successful recanalization of coronary chronic total occlusion Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study Noninvasive Nuclear SPECT Myocardial Blood Flow Quantitation to Guide Management for Coronary Artery Disease Long-term effects of intensive glucose lowering on cardiovascular outcomes 2019 Guidelines on Diabetes, Pre-Diabetes and Cardiovascular Diseases developed in collaboration with the EASD ESC Clinical Practice Guidelines
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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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