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Fractional Flow Reserve–Guided PCI as Compared with Coronary Bypass Surgery Bare metal or drug-eluting stent versus drug-coated balloon in non-ST-elevation myocardial infarction: the randomised PEPCAD NSTEMI trial Chronic Total Occlusion Interventions: Update on Current Tips and Tricks Applications of left ventricular strain measurements to patients undergoing chemotherapy Treating Bifurcation Lesions: The Result Overcomes the Technique Impact of stent deformity induced by the kissing balloon technique for bifurcating lesions on in-stent restenosis after coronary intervention Multicenter Registry of Real-World Patients With Severely Calcified Coronary Lesions Undergoing Orbital Atherectomy: 1-Year Outcomes Percutaneous coronary intervention with drug-coated balloon-only strategy in stable coronary artery disease and in acute coronary syndromes: An all-comers registry study The Hybrid Approach to Chronic Total Occlusion Percutaneous Coronary Intervention: Update From the PROGRESS CTO Registry Percutaneous coronary interventional strategies for treatment of in-stent restenosis: a network meta-analysis

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.