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Anatomical and Functional Computed Tomography for Diagnosing Hemodynamically Significant Coronary Artery Disease: A Meta-Analysis Comparative efficacy of two paclitaxel-coated balloons with different excipient coatings in patients with coronary in-stent restenosis: A pooled analysis of the Intracoronary Stenting and Angiographic Results: Optimizing Treatment of Drug Eluting Stent In-Stent Restenosis 3 and 4 trials Coronary Microcirculation Downstream Non-Infarct-Related Arteries in the Subacute Phase of Myocardial Infarction: Implications for Physiology-Guided Revascularization Treatment of calcified coronary lesions with Palmaz-Schatz stents. An intravascular ultrasound study Cardiotoxicity and Cardiac Monitoring Among Chemotherapy-Treated Breast Cancer Patients Characteristics of abnormal post-stent optical coherence tomography findings in hemodialysis patients Lesion-Specific and Vessel-Related Determinants of Fractional Flow Reserve Beyond Coronary Artery Stenosis Updated clinical classification of pulmonary hypertension Effect of low-density lipoprotein cholesterol on the geometry of coronary bifurcation lesions and clinical outcomes of coronary interventions in the J-REVERSE registry Update on chronic thromboembolic pulmonary hypertension
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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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