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Cardiovascular Magnetic Resonance as a complementary method to Transthoracic Echocardiography for Aortic Valve Area Estimation in patients with Aortic Stenosis: A systematic review and meta-analysis Clinical Outcomes Following Intravascular Imaging-Guided Versus Coronary Angiography-Guided Percutaneous Coronary Intervention With Stent Implantation: A Systematic Review and Bayesian Network Meta-Analysis of 31 Studies and 17,882 Patients The Year in Cardiovascular Medicine 2020: Valvular Heart Disease: Discussing the Year in Cardiovascular Medicine for 2020 in the field of valvular heart disease is Professor Helmut Baumgartner and Dr Javier Bermejo. Mark Nicholls reports A new strategy for discontinuation of dual antiplatelet therapy: the RESET Trial (REal Safety and Efficacy of 3-month dual antiplatelet Therapy following Endeavor zotarolimus-eluting stent implantation) Intravascular ultrasound guidance improves clinical outcomes during implantation of both first- and second-generation drug-eluting stents: a meta-analysis Cardiac and Kidney Benefits of Empagliflozin in Heart Failure Across the Spectrum of Kidney Function: Insights From EMPEROR-Reduced The conductive function of biopolymer corrects myocardial scar conduction blockage and resynchronizes contraction to prevent heart failure Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure Dual-antiplatelet treatment beyond 1 year after drug-eluting stent implantation (ARCTIC-Interruption): a randomised trial Ticagrelor Monotherapy Versus Dual-Antiplatelet Therapy After PCI: An Individual Patient-Level Meta-Analysis
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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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