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Current treatment of significant left main coronary artery disease: A review Two-year outcomes following unprotected left main stenting with first vs new-generation drug-eluting stents: the FINE registry. EuroIntervention. Mechanisms of in-stent restenosis after drug-eluting stent implantation: intravascular ultrasound analysis The Science Underlying COVID-19: Implications for the Cardiovascular System Impact of Intravascular Ultrasound on Long-Term Clinical Outcomes in Patients With Acute Myocardial Infarction Anthracycline-induced cardiotoxicity: A multicenter randomised trial comparing two strategies for guiding prevention with enalapril: The International CardioOncology Society-one trial Risk of Coronary Obstruction and Feasibility of Coronary Access After Repeat Transcatheter Aortic Valve Replacement With the Self-Expanding Evolut Valve: A Computed Tomography Simulation Study Randomized study to evaluate sirolimus-eluting stents implanted at coronary bifurcation lesions Defining a new standard for IVUS optimized drug eluting stent implantation: the PRAVIO study P2Y12 Inhibitor Monotherapy with Clopidogrel Versus Ticagrelor in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention
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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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