CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Dapagliflozin for treating chronic heart failure with reduced ejection fraction Exercise Intolerance in Patients With Heart Failure: JACC State-of-the-Art Review INTERMACS Profiles and Outcomes Among Non–Inotrope-Dependent Outpatients With Heart Failure and Reduced Ejection Fraction The Role of the Pericardium in Heart Failure: Implications for Pathophysiology and Treatment Reduced Apolipoprotein M and Adverse Outcomes Across the Spectrum of Human Heart Failure Prior Pacemaker Implantation and Clinical Outcomes in Patients With Heart Failure and Preserved Ejection Fraction Cardiovascular Events Associated With SGLT-2 Inhibitors Versus Other Glucose-Lowering Drugs: The CVD-REAL 2 Study Noninvasive Imaging for the Evaluation of Diastolic Function: Promises Fulfilled The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study The conductive function of biopolymer corrects myocardial scar conduction blockage and resynchronizes contraction to prevent heart failure
|<<... 9 10 11 12 13 14 15 16 >>|

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