CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Randomized Comparison of Ridaforolimus-Eluting and Zotarolimus-Eluting Coronary Stents 2-Year Clinical Outcomes: From the BIONICS and NIREUS Trials Drug-coated balloons for small coronary artery disease (BASKET-SMALL 2): an open-label randomised non-inferiority trial TACIT (High Sensitivity Troponin T Rules Out Acute Cardiac Insufficiency Trial): An Observational Study to Identify Acute Heart Failure Patients at Low Risk for Rehospitalization or Mortality Canadian Multicenter Chronic Total Occlusion Registry: Ten-Year Follow-Up Results of Chronic Total Occlusion Revascularization Atrial Fibrillation Burden: Moving Beyond Atrial Fibrillation as a Binary Entity: A Scientific Statement From the American Heart Association Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association Older Adults in the Cardiac Intensive Care Unit: Factoring Geriatric Syndromes in the Management, Prognosis, and Process of Care: A Scientific Statement From the American Heart Association Right ventricular stroke work correlates with outcomes in pediatric pulmonary arterial hypertension Transcatheter Mitral-Valve Repair in Patients with Heart Failure State of the Art in Noninvasive Imaging of Ischemic Heart Disease and Coronary Microvascular Dysfunction in Women: Indications, Performance, and Limitations
|<<... 48 49 50 51 52 53 54 ...>>|

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.

>CBS CBS 2019

> CBS 2019

> CBS 2019 注册

> CBS 2019 会议日程

> CBS 2019 学术征集

> CBS 2019 热点

 CBSMD

> CBSMD 网站注册

> 教育中心

> 荐读文献

> Top 10 阅读文献

> 文献导读
CBS 2019 CBS 2019 主席团 教育中心 科研动态
Copyright ⓒ CBS MD Nanjing China. All rights reserved. 京ICP备16025898号-11
联系我们| Top