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充血性心力衰竭

科研文章

荐读文献

INTERMACS Profiles and Outcomes Among Non–Inotrope-Dependent Outpatients With Heart Failure and Reduced Ejection Fraction Impact of epicardial adipose tissue on cardiovascular haemodynamics, metabolic profile, and prognosis in heart failure Randomized Evaluation of Heart Failure With Preserved Ejection Fraction Patients With Acute Heart Failure and Dopamine - The ROPA-DOP Trial Prior Pacemaker Implantation and Clinical Outcomes in Patients With Heart Failure and Preserved Ejection Fraction Criteria for Iron Deficiency in Patients With Heart Failure Reduced Apolipoprotein M and Adverse Outcomes Across the Spectrum of Human Heart Failure The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study Effect of SGLT2-Inhibitors on Epicardial Adipose Tissue: A Meta-Analysis Clinical Phenogroups in Heart Failure With Preserved Ejection Fraction: Detailed Phenotypes, Prognosis, and Response to Spironolactone Noninvasive Imaging for the Evaluation of Diastolic Function: Promises Fulfilled

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.