CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Association Between Diastolic Dysfunction and Health Status Outcomes in Patients Undergoing Transcatheter Aortic Valve Replacement 1-Year Outcomes of Patients Undergoing Primary Angioplasty for Myocardial Infarction Treated With Prasugrel Versus Ticagrelor Risk of Myocardial Infarction in Anticoagulated Patients With Atrial Fibrillation Off-hour presentation and outcomes in patients with acute myocardial infarction: systematic review and meta-analysis Canadian SCAD Cohort Study: Shedding Light on SCAD From a United Front Prognostic Value of Fractional Flow Reserve Measured Immediately After Drug-Eluting Stent Implantation Impact of age and comorbidity on risk stratification in idiopathic pulmonary arterial hypertension A sirolimus-eluting bioabsorbable polymer-coated stent (MiStent) versus an everolimus-eluting durable polymer stent (Xience) after percutaneous coronary intervention (DESSOLVE III): a randomised, single-blind, multicentre, non-inferiority, phase 3 trial Long-Term Coronary Functional Assessment of the Infarct-Related Artery Treated With Everolimus-Eluting Bioresorbable Scaffolds or Everolimus-Eluting Metallic Stents: Insights of the TROFI II Trial A Novel Circulating MicroRNA for the Detection of Acute Myocarditis

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.