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A Controlled Trial of Rivaroxaban After Transcatheter Aortic-Valve Replacement Comparison of Early Surgical or Transcatheter Aortic Valve Replacement Versus Conservative Management in Low-Flow, Low-Gradient Aortic Stenosis Using Inverse Probability of Treatment Weighting: Results From the TOPAS Prospective Observational Cohort Study Anticoagulation After Surgical or Transcatheter Bioprosthetic Aortic Valve Replacement Balloon Aortic Valvuloplasty as a Bridge to Aortic Valve Replacement: A Contemporary Nationwide Perspective A Review of the Role of Breast Arterial Calcification for Cardiovascular Risk Stratification in Women 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk: The Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS) Association of Coronary Artery Calcium With Long-term, Cause-Specific Mortality Among Young Adults Management of Asymptomatic Severe Aortic Stenosis: Evolving Concepts in Timing of Valve Replacement Pulmonary arterial hypertension in congenital heart disease: an epidemiologic perspective from a Dutch registry The contribution of tissue-grouped BMI-associated gene sets to cardiometabolic-disease risk: a Mendelian randomization 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.