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Association of Circulating Monocyte Chemoattractant Protein-1 Levels With Cardiovascular Mortality: A Meta-analysis of Population-Based Studies Switching of Oral Anticoagulation Therapy After PCI in Patients With Atrial Fibrillation: The RE-DUAL PCI Trial Subanalysis Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes Relation between baseline plaque features and subsequent coronary artery remodeling determined by optical coherence tomography and intravascular ultrasound Five-Year Outcomes of Transcatheter or Surgical Aortic-Valve Replacement Cardiac Structural Changes After Transcatheter Aortic Valve Replacement: Systematic Review and Meta-Analysis of Cardiovascular Magnetic Resonance Studies Association of Reduced Apical Untwisting With Incident HF in Asymptomatic Patients With HF Risk Factors INTERMACS Profiles and Outcomes Among Non–Inotrope-Dependent Outpatients With Heart Failure and Reduced Ejection Fraction Utility of intravascular ultrasound guidance in patients undergoing percutaneous coronary intervention for type C lesions The effect of complete percutaneous revascularisation with and without intravascular ultrasound guidance in the drugeluting stent era

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.