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Revascularization in Patients With Left Main Coronary Artery Disease and Left Ventricular Dysfunction Comparison of inhospital mortality, length of hospitalization, costs, and vascular complications of percutaneous coronary interventions guided by ultrasound versus angiography 3-Year Outcomes of the ULTIMATE Trial Comparing Intravascular Ultrasound Versus Angiography-Guided Drug-Eluting Stent Implantation Contribution of stent underexpansion to recurrence after sirolimus-eluting stent implantation for in-stent restenosis Successful bailout stenting strategy against lethal coronary dissection involving left main bifurcation Criteria for Iron Deficiency in Patients With Heart Failure Genotyping to Guide Clopidogrel Treatment: An In-Depth Analysis of the TAILOR-PCI Trial Transcatheter Aortic Valve Replacement vs Surgical Replacement in Patients With Pure Aortic Insufficiency Longitudinal Assessment of Vascular Function With Sunitinib in Patients With Metastatic Renal Cell Carcinoma Why and How to Measure Aortic Valve Calcification in Patients With Aortic Stenosis

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.