CBS 2019
CBSMD教育中心
中 文

Congestive Heart Failure

Abstract

Recommended Article

Wireless pulmonary artery pressure monitoring guides management to reduce decompensation in heart failure with preserved ejection fraction Improving the Use of Primary Prevention Implantable Cardioverter-Defibrillators Therapy With Validated Patient-Centric Risk Estimates In acute HF and iron deficiency, IV ferric carboxymaltose reduced HF hospitalizations, but not CV death, at 1 y Ejection Fraction Pros and Cons: JACC State-of-the-Art Review 2021 ACC/AHA Key Data Elements and Definitions for Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Heart Failure) Frequency, predictors, and prognosis of ejection fraction improvement in heart failure: an echocardiogram-based registry study Proteomics to Improve Phenotyping in Obese Patients with Heart Failure with Preserved Ejection Fraction Cardiac resynchronization therapy with a defibrillator (CRTd) in failing heart patients with type 2 diabetes mellitus and treated by glucagon-like peptide 1 receptor agonists (GLP-1 RA) therapy vs. conventional hypoglycemic drugs: arrhythmic burden, hospitalizations for heart failure, and CRTd responders rate

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.