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Screening for Atrial Fibrillation With Electrocardiography US Preventive Services Task Force Recommendation Statement Changes in high-sensitivity troponin after drug-coated balloon angioplasty for drug-eluting stent restenosis Optimal medical therapy improves clinical outcomes in patients undergoing revascularization with percutaneous coronary intervention or coronary artery bypass grafting: insights from the Synergy Between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery (SYNTAX) trial at the 5-year follow-up Healthy Behavior, Risk Factor Control, and Survival in the COURAGE Trial Alirocumab Reduces Total Nonfatal Cardiovascular and Fatal Events in the ODYSSEY OUTCOMES Trial Effects of Liraglutide on Cardiovascular Outcomes in Patients With Diabetes With or Without Heart Failure Aspirin in the primary and secondary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials Transverse partial stent ablation with rotational atherectomy for suboptimal culotte technique in left main stem bifurcation Effect of Side Branch Predilation in Coronary Bifurcation Stenting With the Provisional Approach - Results From the COBIS (Coronary Bifurcation Stenting) II Registry Positive remodelling of coronary arteries on computed tomography coronary angiogram: an observational study

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.