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Prospective Elimination of Distal Coronary Sinus to Left Atrial Connection for Atrial Fibrillation Ablation (PRECAF) Randomized Controlled Trial LOX-1 in Atherosclerosis and Myocardial Ischemia: Biology, Genetics, and Modulation Defining Staged Procedures for Percutaneous Coronary Intervention Trials A Guidance Document Acute Noncardiac Organ Failure in Acute Myocardial Infarction With Cardiogenic Shock Cardiovascular Mortality After Type 1 and Type 2 Myocardial Infarction in Young Adults Association of Parenteral Anticoagulation Therapy With Outcomes in Chinese Patients Undergoing Percutaneous Coronary Intervention for Non-ST-Segment Elevation Acute Coronary Syndrome 2014 ESC/EACTS Guidelines on myocardial revascularization: The Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)Developed with the special contribution of the European Association of Percutaneous Ca 2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: The Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT) Clinical Significance of Concordance or Discordance Between Fractional Flow Reserve and Coronary Flow Reserve for Coronary Physiological Indices, Microvascular Resistance, and Prognosis After Elective Percutaneous Coronary Intervention Evidence-based detection of pulmonary arterial hypertension in systemic sclerosis: the DETECT 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.