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Safety of intermediate left main stenosis revascularization deferral based on fractional flow reserve and intravascular ultrasound: A systematic review and meta-regression including 908 deferred left main stenosis from 12 studies Transcatheter Aortic Valve Implantation Represents an Anti-Inflammatory Therapy Via Reduction of Shear Stress–Induced, Piezo-1–Mediated Monocyte Activation In vitro flow and optical coherence tomography comparison of two bailout techniques after failed provisional stenting for bifurcation percutaneous coronary interventions Percutaneous coronary intervention in left main coronary artery disease: the 13th consensus document from the European Bifurcation Club Self-expandable sirolimus-eluting stents compared to second-generation drug-eluting stents for the treatment of the left main: A propensity score analysis from the SPARTA and the FAILS-2 registries Coronary Microcirculation in Ischemic Heart Disease 2-Year Outcomes After Transcatheter Versus Surgical Aortic Valve Replacement in Low-Risk Patients Transcatheter Versus Surgical Aortic Valve Replacement in Patients With Rheumatic Aortic Stenosis A randomized clinical study comparing double kissing crush with provisional stenting for treatment of coronary bifurcation lesions: results from the DKCRUSH-II (Double Kissing Crush versus Provisional Stenting Technique for Treatment of Coronary Bifurcation Lesions) trial Advances in therapeutic interventions for patients with pulmonary arterial hypertension

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.