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Long-term safety and effectiveness of unprotected left main coronary stenting with drug-eluting stents compared with bare-metal stents Left main coronary angioplasty: early and late results of 127 acute and elective procedures Unprotected Left Main Disease: Indications and Optimal Strategies for Percutaneous Intervention Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE Registry Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation Influence of Heart Rate on FFR Measurements: An Experimental and Clinical Validation Study Impact of myocardial supply area on the transstenotic hemodynamics as determined by fractional flow reserve Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW A prediction model of simple echocardiographic variables to screen for potentially correctable shunts in adult patients with pulmonary arterial hypertension associated with atrial septal defects: a cross-sectional study OCT compared with IVUS in a coronary lesion assessment: the OPUS-CLASS study
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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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