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Phosphoproteomic Analysis of Neonatal Regenerative Myocardium Revealed Important Roles of CHK1 via Activating mTORC1/P70S6K Pathway Relations between implementation of new treatments and improved outcomes in patients with non-ST-elevation myocardial infarction during the last 20 years: experiences from SWEDEHEART registry 1995 to 2014 Post-Discharge Bleeding and Mortality Following Acute Coronary Syndromes With or Without PCI Clinical and Angiographic Features of Patients With Out-of-Hospital Cardiac Arrest and Acute Myocardial Infarction Coronary CT Angiography and 5-Year Risk of Myocardial Infarction Comparison of the Preventive Efficacy of Rosuvastatin Versus Atorvastatin in Post-Contrast Acute Kidney Injury in Patients With ST-segment Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention Coronary Angiography in Patients With Out-of-Hospital Cardiac Arrest Without ST-Segment Elevation: A Systematic Review and Meta-Analysis Acute Myocardial Injury in Patients Hospitalized With COVID-19 Infection: A Review Role of Low Endothelial Shear Stress and Plaque Characteristics in the Prediction of Nonculprit Major Adverse Cardiac Events: The PROSPECT Study OPTIMAL USE OF LIPID-LOWERING THERAPY AFTER ACUTE CORONARY SYNDROMES: A Position Paper endorsed by the International Lipid Expert Panel (ILEP)
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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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