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Ablation Versus Drug Therapy for Atrial Fibrillation in Heart Failure Results From the CABANA Trial Endoplasmic reticulum stress in doxorubicin-induced cardiotoxicity may be therapeutically targeted by natural and chemical compounds: A review Does pulsed field ablation regress over time? A quantitative temporal analysis of pulmonary vein isolation Percutaneous Left Atrial Appendage Occlusion for Patients in Atrial Fibrillation Suboptimal for Warfarin Therapy: 5-year Results of the PLAATO (Percutaneous Left Atrial Appendage Transcatheter Occlusion) Study The management of secondary mitral regurgitation in patients with heart failure: a joint position statement from the Heart Failure Association (HFA), European Association of Cardiovascular Imaging (EACVI), European Heart Rhythm Association (EHRA), and European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC Current Status and Future Prospects of Transcatheter Mitral Valve Replacement: JACC State-of-the-Art Review Prospective Evaluation of Transseptal TMVR for Failed Surgical Bioprostheses: MITRAL Trial Valve-in-Valve Arm 1-Year Outcomes Single direct oral anticoagulant therapy in stable patients with atrial fibrillation beyond 1 year after coronary stent implantation 2015 ACC/HRS/SCAI Left Atrial Appendage Occlusion Device Societal Overview Systematic Review and Network Meta‐Analysis Comparing Bifurcation Techniques for Percutaneous Coronary Intervention

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.