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Association Between Malignant Mitral Valve Prolapse and Sudden Cardiac Death: A Review Italian Society of Interventional Cardiology (GIse) Registry Of Transcatheter Treatment of Mitral Valve RegurgitaTiOn (GIOTTO): Impact of Valve Disease Etiology and Residual Mitral Regurgitation after MitraClip Implantation Initial experience with percutaneous mitral valve repair in patients with cardiac amyloidosis Transcatheter Interventions for Tricuspid Valve Disease: What to Do and Who to Do it On Closure of Iatrogenic Atrial Septal Defect Following Transcatheter Mitral Valve Repair: The Randomized MITHRAS Trial Risk of Atrial Fibrillation According to Cancer Type: A Nationwide Population-Based Study Italian Society of Interventional Cardiology (GIse) Registry Of Transcatheter Treatment of Mitral Valve RegurgitaTiOn (GIOTTO): Impact of Valve Disease Etiology and Residual Mitral Regurgitation after MitraClip Implantation Percutaneous left atrial appendage occlusion: the Munich consensus document on definitions, endpoints, and data collection requirements for clinical studies Novel Transcatheter Mitral Valve Prosthesis for Patients With Severe Mitral Annular Calcification The Tricuspid Annular Plane Systolic Excursion to Systolic Pulmonary Artery Pressure Index: Association With All-Cause Mortality in Patients With Moderate or Severe Tricuspid Regurgitation
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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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