CBS 2019
CBSMD教育中心
中 文

Congestive Heart Failure

Abstract

Recommended Article

Respiratory Syncytial Virus and Associations With Cardiovascular Disease in Adults Design of a bilevel clinical trial targeting adherence in heart failure patients and their providers: The Congestive Heart Failure Adherence Redesign Trial (CHART) Rationale and design of the comParIson Of sacubitril/valsartaN versus Enalapril on Effect on nt-pRo-bnp in patients stabilized from an acute Heart Failure episode (PIONEER-HF) trial Angiotensin-Neprilysin Inhibition in Acute Decompensated Heart Failure Clinical trial design and rationale of the Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy With HeartMate 3 (MOMENTUM 3) investigational device exemption clinical study protocol Good response to tolvaptan shortens hospitalization in patients with congestive heart failure Two-Year Outcomes with a Magnetically Levitated Cardiac Pump in Heart Failure The year in cardiovascular medicine 2020: heart failure and cardiomyopathies

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.