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The Year in Cardiovascular Medicine 2020: Coronary Prevention: Looking back on the Year in Cardiovascular Medicine for 2020 in the field of coronary prevention is Professor Ramon Estruch, Dr Luis Ruilope, and Professor Francesco Cosentino. Mark Nicholls meets them ACC/AHA Versus ESC Guidelines on Dual Antiplatelet Therapy JACC Guideline Comparison: JACC State-of-the-Art Review Clinical Phenogroups in Heart Failure With Preserved Ejection Fraction: Detailed Phenotypes, Prognosis, and Response to Spironolactone Transcatheter Aortic Valve Implantation Represents an Anti-Inflammatory Therapy Via Reduction of Shear Stress-Induced, Piezo-1-Mediated Monocyte Activation 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure Haptoglobin genotype: a determinant of cardiovascular complication risk in type 1 diabetes Noninvasive Imaging for the Evaluation of Diastolic Function: Promises Fulfilled Proteomics to Improve Phenotyping in Obese Patients with Heart Failure with Preserved Ejection Fraction Effects of Icosapent Ethyl on Total Ischemic Events: From REDUCE-IT Baseline Characteristics and Risk Profiles of Participants in the ISCHEMIA Randomized Clinical Trial

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.