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Retrospective Comparison of Long-Term Clinical Outcomes Between Percutaneous Coronary Intervention and Medical Therapy in Stable Coronary Artery Disease With Gray Zone Fractional Flow Reserve - COMFORTABLE Retrospective Study Impact of an optical coherence tomography guided approach in acute coronary syndromes: A propensity matched analysis from the international FORMIDABLE-CARDIOGROUP IV and USZ registry Optical Coherence Tomography-Guided Percutaneous Coronary Intervention in ST-Segment-Elevation Myocardial Infarction: A Prospective Propensity-Matched Cohort of the Thrombectomy Versus Percutaneous Coronary Intervention Alone Trial A Randomized Trial Evaluating Online 3-Dimensional Optical Frequency Domain Imaging-Guided Percutaneous Coronary Intervention in Bifurcation Lesions Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty Left main coronary artery compression in pulmonary hypertension Rotational Atherectomy in acute STEMI with heavily calcified culprit lesion is a rule breaking solution Robustness of Fractional Flow Reserve for Lesion Assessment in Non-Infarct-Related Arteries of Patients With Myocardial Infarction Optimal threshold of postintervention minimum stent area to predict in-stent restenosis in small coronary arteries: An optical coherence tomography analysis PCI for obstructive bifurcation lesions the 14th consensus document from the european bifurcation club
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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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