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The Wait for High-Sensitivity Troponin Is Over—Proceed Cautiously Impact of the US Food and Drug Administration–Approved Sex-Specific Cutoff Values for High-Sensitivity Cardiac Troponin T to Diagnose Myocardial Infarction Clinical Outcomes Following Intravascular Imaging-Guided Versus Coronary Angiography–Guided Percutaneous Coronary Intervention With Stent Implantation: A Systematic Review and Bayesian Network Meta-Analysis of 31 Studies and 17,882 Patients Good response to tolvaptan shortens hospitalization in patients with congestive heart failure Cardiac Implantable Electronic Devices in Patients With Left Ventricular Assist Systems Revision: prognostic impact of baseline glucose levels in acute myocardial infarction complicated by cardiogenic shock-a substudy of the IABP-SHOCK II-trial Usefulness of longitudinal reconstructed optical coherence tomography images for predicting the need for the reverse wire technique during coronary bifurcation interventions Wearable Cardioverter-Defibrillator after Myocardial Infarction Can We Use the Intrinsic Left Ventricular Delay (QLV) to Optimize the Pacing Configuration for Cardiac Resynchronization Therapy With a Quadripolar Left Ventricular Lead? Use of Risk Assessment Tools to Guide Decision-Making in the Primary Prevention of Atherosclerotic Cardiovascular Disease A Special Report From the American Heart Association and American College of Cardiology

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.