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Prognostic impact of atrial fibrillation in cardiogenic shock complicating acute myocardial infarction: a substudy of the IABP-SHOCK II trial Relationship Between Infarct Size and Outcomes Following Primary PCI: Patient-Level Analysis From 10 Randomized Trials Intravascular ultrasound-guided percutaneous coronary intervention in left main coronary bifurcation lesions: a review Predicting lifetime risk for developing atherosclerotic cardiovascular disease in Chinese population: the China-PAR project Prognostic impact of non-culprit chronic total occlusions in infarct-related cardiogenic shock: results of the randomised IABP-SHOCK II trial Risk of Early Adverse Events After Clopidogrel Discontinuation in Patients Undergoing Short-Term Dual Antiplatelet Therapy: An Individual Participant Data Analysis Risk Stratification for Patients in Cardiogenic Shock After Acute Myocardial Infarction Non-eligibility for reperfusion therapy in patients presenting with ST-segment elevation myocardial infarction: Contemporary insights from the National Cardiovascular Data Registry (NCDR) Intracoronary Optical Coherence Tomography 2018: Current Status and Future Directions Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China)
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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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