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What's new in the Fourth Universal Definition of Myocardial infarction? Left Main Revascularization in 2017 Coronary Artery Bypass Grafting or Percutaneous Coronary Intervention? Prognostic Significance of Complex Ventricular Arrhythmias Complicating ST-Segment Elevation Myocardial Infarction Patterns and associations between DAPT cessation and 2-year clinical outcomes in left main/proximal LAD versus other PCI: Results from the Patterns of Non-Adherence to Dual Antiplatelet Therapy in Stented Patients (PARIS) registry Comparison of double kissing crush versus Culotte stenting for unprotected distal left main bifurcation lesions: results from a multicenter, randomized, prospective DKCRUSH-III study Relation between door-to-balloon times and mortality after primary percutaneous coronary intervention over time: a retrospective study Respiratory syncytial virus infection and risk of acute myocardial infarction Improvement of Clinical Outcome in Patients With ST-Elevation Myocardial Infarction Between 1999 And 2016 in China : The Prospective, Multicenter Registry MOODY Study In Vivo Calcium Detection by Comparing Optical Coherence Tomography, Intravascular Ultrasound, and Angiography Recurrent Cardiovascular Events in Survivors of Myocardial Infarction with St-Segment Elevation (From the AMI-QUEBEC Study)
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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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