CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Haemodynamic definitions and updated clinical classification of pulmonary hypertension Residual Shunt After Patent Foramen Ovale Closure and Long-Term Stroke Recurrence: A Prospective Cohort Study Transcatheter Aortic Valve Implantation Represents an Anti-Inflammatory Therapy Via Reduction of Shear Stress-Induced, Piezo-1-Mediated Monocyte Activation Role of endothelial dysfunction in determining angina after percutaneous coronary intervention: Learning from pathophysiology to optimize treatment Role of local coronary blood flow patterns and shear stress on the development of microvascular and epicardial endothelial dysfunction and coronary plaque Ticagrelor alone versus ticagrelor plus aspirin from month 1 to month 12 after percutaneous coronary intervention in patients with acute coronary syndromes (ULTIMATE-DAPT): a randomised, placebo-controlled, double-blind clinical trial Clinical Impact of Residual Leaks Following Left Atrial Appendage Occlusion: Insights From the NCDR LAAO Registry De-escalating Dual Antiplatelet Therapy to Ticagrelor Monotherapy in Acute Coronary Syndrome : A Systematic Review and Individual Patient Data Meta-analysis of Randomized Clinical Trials Half-Dose Direct Oral Anticoagulation Versus Standard Antithrombotic Therapy After Left Atrial Appendage Occlusion A Score to Assess Mortality After Percutaneous Mitral Valve Repair

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.