CBS 2019
CBSMD教育中心
中 文

Congestive Heart Failure

Abstract

Recommended Article

The prevalence and importance of frailty in heart failure with reduced ejection fraction - an analysis of PARADIGM-HF and ATMOSPHERE Evaluation and Management of Right-Sided Heart Failure: A Scientific Statement From the American Heart Association Positive recommendation for angiotensin receptor/neprilysin inhibitor: First medication approval for heart failure without "reduced ejection fraction" The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure Universal Definition and Classification of Heart Failure: A Report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure Natriuretic Peptide-Guided Heart Failure Therapy After the GUIDE-IT Study Nocturnal thoracic volume overload and post-discharge outcomes in patients hospitalized for acute heart failure Clinical applications of machine learning in the diagnosis, classification, and prediction of 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.