CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Nocturnal thoracic volume overload and post-discharge outcomes in patients hospitalized for acute heart failure Metformin Lowers Body Weight But Fails to Increase Insulin Sensitivity in Chronic Heart Failure Patients without Diabetes: a Randomized, Double-Blind, Placebo-Controlled Study Cardiac resynchronization therapy with a defibrillator (CRTd) in failing heart patients with type 2 diabetes mellitus and treated by glucagon-like peptide 1 receptor agonists (GLP-1 RA) therapy vs. conventional hypoglycemic drugs: arrhythmic burden, hospitalizations for heart failure, and CRTd responders rate Lateral Wall Dysfunction Signals Onset of Progressive Heart Failure in Left Bundle Branch Block Longitudinal Change in Galectin-3 and Incident Cardiovascular Outcomes Lifestyle Modifications for Preventing and Treating Heart Failure Exercise Intolerance in Patients With Heart Failure: JACC State-of-the-Art Review Heart Failure With Recovered Left Ventricular Ejection Fraction: JACC Scientific Expert Panel A Randomized Controlled Trial to Evaluate the Safety and Efficacy of Cardiac Contractility Modulation Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure

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.