CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Pharmacotherapy in the Management of Anxiety and Pain During Acute Coronary Syndromes and the Risk of Developing Symptoms of Posttraumatic Stress Disorder Appropriate Use Criteria and Health Status Outcomes Following Chronic Total Occlusion Percutaneous Coronary Intervention: Insights From the OPEN-CTO Registry Invasive Management of Acute Myocardial Infarction Complicated by Cardiogenic Shock: A Scientific Statement From the American Heart Association Genetic dysregulation of endothelin-1 is implicated in coronary microvascular dysfunction Outcome of Applying the ESC 0/1-hour Algorithm in Patients With Suspected Myocardial Infarction Long-term outcomes after myocardial infarction in middle-aged and older patients with congenital heart disease-a nationwide study Mechanisms and diagnostic evaluation of persistent or recurrent angina following percutaneous coronary revascularization A Test in Context: E/A and E/e' to Assess Diastolic Dysfunction and LV Filling Pressure Association of Body Mass Index With Lifetime Risk of Cardiovascular Disease and Compression of Morbidity Invasive Versus Medical Management in Patients With Prior Coronary Artery Bypass Surgery With a Non-ST Segment Elevation Acute Coronary Syndrome: A Pilot Randomized Controlled Trial
|<<... 41 42 43 44 45 46 47 ...>>|

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