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充血性心力衰竭

科研文章

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Unexpectedly Low Natriuretic Peptide Levels in Patients With Heart Failure Atrial Fibrillation and the Prognostic Performance of Biomarkers in Heart Failure From ACE Inhibitors/ARBs to ARNIs in Coronary Artery Disease and Heart Failure (Part 2/5) Association of Left Ventricular Systolic Function With Incident Heart Failure in Late Life Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes Mechanical circulatory support devices in advanced heart failure: 2020 and beyond Mechanical circulatory support devices for acute right ventricular failure Age-Related Characteristics and Outcomes of Patients With Heart Failure With Preserved Ejection Fraction Type 2 Diabetes Mellitus and Heart Failure: A Scientific Statement From the American Heart Association and the Heart Failure Society of America Natriuretic Peptide-Guided Heart Failure Therapy After the GUIDE-IT Study

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.