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Adenosine and adenosine receptor-mediated action in coronary microcirculation Infective Endocarditis After Transcatheter Aortic Valve Replacement Differences between the left main and other bifurcations Ascending Aortic Length and Risk of Aortic Adverse Events: The Neglected Dimension Intravascular ultrasound-guided percutaneous coronary intervention improves the clinical outcome in patients undergoing multiple overlapping drug-eluting stents implantation New-onset atrial fibrillation after PCI and CABG for left main disease: insights from the EXCEL trial and additional studies Decline in Left Ventricular Ejection Fraction During Follow-Up in Patients With Severe Aortic Stenosis Comparative effectiveness analysis of percutaneous coronary intervention versus coronary artery bypass grafting in patients with chronic kidney disease and unprotected left main coronary artery disease Surgical ineligibility and mortality among patients with unprotected left main or multivessel coronary artery disease undergoing percutaneous coronary intervention Active SB-P Versus Conventional Approach to the Protection of High-Risk Side Branches: The CIT-RESOLVE Trial

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.