CBS 2019
CBSMD教育中心
中 文

Congestive Heart Failure

Abstract

Recommended Article

From ACE Inhibitors/ARBs to ARNIs in Coronary Artery Disease and Heart Failure (Part 2/5) How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) Metformin Lowers Body Weight But Fails to Increase Insulin Sensitivity in Chronic Heart Failure Patients without Diabetes: a Randomized, Double-Blind, Placebo-Controlled Study Heart Failure With Mid-Range (Borderline) Ejection Fraction: Clinical Implications and Future Directions Heart Failure and Atrial Fibrillation, Like Fire and Fury Haemodynamic-guided management of heart failure (GUIDE-HF): a randomised controlled trial 2019 ACC Expert Consensus Decision Pathway on Risk Assessment, Management, and Clinical Trajectory of Patients Hospitalized With Heart Failure A Report of the American College of Cardiology Solution Set Oversight Committee Dapagliflozin Effects on Biomarkers, Symptoms, and Functional Status in Patients With Heart Failure With Reduced Ejection Fraction: The DEFINE-HF Trial

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.