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充血性心力衰竭

科研文章

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Aliskiren, Enalapril, or Aliskiren and Enalapril in Heart Failure Modifiable lifestyle factors and heart failure: A Mendelian randomization study A trial to evaluate the effect of the sodium-glucose co-transporter 2 inhibitor dapagliflozin on morbidity and mortality in patients with heart failure and reduced left ventricular ejection fraction (DAPA-HF) The prevalence and importance of frailty in heart failure with reduced ejection fraction - an analysis of PARADIGM-HF and ATMOSPHERE Empagliflozin Increases Cardiac Energy Production in Diabetes - Novel Translational Insights Into the Heart Failure Benefits of SGLT2 Inhibitors Primary Prevention of Heart Failure in Women The Management of Atrial Fibrillation in Heart Failure: An Expert Panel Consensus Heart Failure and Atrial Fibrillation, Like Fire and Fury Sex- and Race-Related Differences in Characteristics and Outcomes of Hospitalizations for Heart Failure With Preserved Ejection Fraction Dilated cardiomyopathy: so many cardiomyopathies!

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.