CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Dapagliflozin Effects on Biomarkers, Symptoms, and Functional Status in Patients With Heart Failure With Reduced Ejection Fraction: The DEFINE-HF Trial Effect of Empagliflozin on Cardiovascular and Renal Outcomes in Patients With Heart Failure by Baseline Diabetes Status - Results from the EMPEROR-Reduced Trial Vericiguat in Patients with Heart Failure and Reduced Ejection Fraction Cardiovascular Aging and Heart Failure: JACC Review Topic of the Week Left Ventricular Assist Devices: Synergistic Model Between Technology and Medicine Vaccination Trends in Patients With Heart Failure - Insights From Get With The Guidelines–Heart Failure Antithrombotics From Aspirin to DOACs in Coronary Artery Disease and Atrial Fibrillation (Part 3/5) Ejection Fraction Pros and Cons: JACC State-of-the-Art Review Clinical epidemiology of heart failure with preserved ejection fraction (HFpEF) in comparatively young hospitalized patients Heart Failure With Preserved Ejection Fraction in the Young

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.