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Advances in Clinical Cardiology 2020: A Summary of Key Clinical Trials Effect of Smoking on Outcomes of Primary PCI in Patients With STEMI Revascularization Strategies in STEMI with Multivessel Disease: Deciding on Culprit Versus Complete-Ad Hoc or Staged Hospital Readmission After Perioperative Acute Myocardial Infarction Associated With Noncardiac Surgery Decreased inspired oxygen stimulates de novo formation of coronary collaterals in adult heart Impact of door-to-balloon time on long-term mortality in high- and low-risk patients with ST-elevation myocardial infarction 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines Association between Coronary Collaterals and Myocardial Viability in Patients with a Chronic Total Occlusion Relationship between therapeutic effects on infarct size in acute myocardial infarction and therapeutic effects on 1-year outcomes: A patient-level analysis of randomized clinical trials Incidence and Outcomes of Acute Coronary Syndrome After Transcatheter Aortic Valve Replacement

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.