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充血性心力衰竭

科研文章

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Longitudinal Change in Galectin-3 and Incident Cardiovascular Outcomes When and how to use SGLT2 inhibitors in patients with HFrEF or chronic kidney disease Association of Cardiovascular Disease With Respiratory Disease The conductive function of biopolymer corrects myocardial scar conduction blockage and resynchronizes contraction to prevent heart failure Dapagliflozin for treating chronic heart failure with reduced ejection fraction AIM2-driven inflammasome activation in heart failure The Role of the Pericardium in Heart Failure: Implications for Pathophysiology and Treatment A randomized controlled trial to evaluate the safety and efficacy of cardiac contractility modulation in patients with systolic heart failure: rationale, design, and baseline patient characteristics. Exercise Intolerance in Patients With Heart Failure: JACC State-of-the-Art Review Evaluation and Management of Right-Sided Heart Failure: A Scientific Statement From the American Heart Association

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.