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Age-Related Characteristics and Outcomes of Patients With Heart Failure With Preserved Ejection Fraction Atherosclerotic plaque with ultrasonic attenuation affects coronary reflow and infarct size in patients with acute coronary syndrome: an intravascular ultrasound study Sex- and Race-Related Differences in Characteristics and Outcomes of Hospitalizations for Heart Failure With Preserved Ejection Fraction Primary Prevention of Heart Failure in Women Mechanical circulatory support devices for acute right ventricular failure Myocardial bridging: contemporary understanding of pathophysiology with implications for diagnostic and therapeutic strategies Timing of Intervention in Aortic Stenosis Burden of Cardiovascular Diseases in China, 1990-2016: Findings From the 2016 Global Burden of Disease Study Guideline‐Directed Medical Therapy for Patients With Heart Failure With Midrange Ejection Fraction: A Patient‐Pooled Analysis From the KorHF and KorAHF Registries In-stent neoatherosclerosis: a final common pathway of late stent failure

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.