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充血性心力衰竭

科研文章

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Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery Effect of Luseogliflozin on Heart Failure With Preserved Ejection Fraction in Patients With Diabetes Mellitus Atrial Fibrillation and the Prognostic Performance of Biomarkers in Heart Failure How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) Modifiable lifestyle factors and heart failure: A Mendelian randomization study Unexpectedly Low Natriuretic Peptide Levels in Patients With Heart Failure Association of Left Ventricular Systolic Function With Incident Heart Failure in Late Life The year in cardiology: heart failure: The year in cardiology 2019 When and how to use SGLT2 inhibitors in patients with HFrEF or chronic kidney disease Nitrosative stress drives heart failure with preserved ejection fraction

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.