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Optical coherence tomography predictors of target vessel myocardial infarction after provisional stenting in patients with coronary bifurcation disease Genetic analyses in a cohort of 191 pulmonary arterial hypertension patients Immunotherapy of Endothelin-1 Receptor Type A for Pulmonary Arterial Hypertension Clinical Outcomes Following Coronary Bifurcation PCI Techniques: A Systematic Review and Network Meta-Analysis Comprising 5,711 Patients Anatomical Attributes of Clinically Relevant Diagonal Branches in Patients with Left Anterior Descending Coronary Artery Bifurcation Lesions Tips of the dual-lumen microcatheter-facilitated reverse wire technique in percutaneous coronary interventions for markedly angulated bifurcated lesions Noninvasive Screening for Pulmonary Hypertension by Exercise Testing in Congenital Heart Disease Coronary Flow Reserve in the Instantaneous Wave-Free Ratio/Fractional Flow Reserve Era: Too Valuable to Be Neglected The Impact of Coronary Physiology on Contemporary Clinical Decision Making Coronary Physiology in the Cardiac Catheterization Laboratory
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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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