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'Ticagrelor alone vs. dual antiplatelet therapy from 1 month after drug-eluting coronary stenting among patients with STEMI': a post hoc analysis of the randomized GLOBAL LEADERS trial Comparison of 1-month Versus 12-month Dual Antiplatelet Therapy after Implantation of Drug-eluting Stents Guided by either Intravascular Ultrasound or Angiography in Patients with Acute Coronary Syndrome: Rationale and Design of Prospective, Multicenter, Randomized, Controlled IVUS-ACS & ULTIMATE-DAPT trial Plaque Rupture, compared to Plaque Erosion, is associated with Higher Level of Pan-coronary Inflammation Pulmonary Artery Denervation Attenuates Pulmonary Arterial Remodeling in Dogs With Pulmonary Arterial Hypertension Induced by Dehydrogenized Monocrotaline Drug-coated balloon for treatment of de-novo coronary artery lesions in patients with high bleeding risk (DEBUT): a single-blind, randomised, non-inferiority trial Acute Aortic Syndrome Revisited: JACC State-of-the-Art Review SR-B1 Drives Endothelial Cell LDL Transcytosis via DOCK4 to Promote Atherosclerosis Frailty in Older Adults Undergoing Aortic Valve Replacement: The FRAILTY-AVR Study Safety and Efficacy of Transcatheter Aortic Valve Replacement With Continuation of Vitamin K Antagonists or Direct Oral Anticoagulants Left Main Percutaneous Coronary Intervention Versus Coronary Artery Bypass Grafting in Patients With Prior Cerebrovascular Disease: Results From the EXCEL Trial
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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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