CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

rhACE2 Therapy Modifies Bleomycin-Induced Pulmonary Hypertension via Rescue of Vascular Remodeling Effect of SGLT2-Inhibitors on Epicardial Adipose Tissue: A Meta-Analysis Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy Intravascular Ultrasound Parameters Associated With Stent Thrombosis After Drug-Eluting Stent Deployment Raising the Evidentiary Bar for Guideline Recommendations for TAVR: JACC Review Topic of the Week Quality of Life after Everolimus-Eluting Stents or Bypass Surgery for Treatment of Left Main Disease 2015 ESC Guidelines for the management of infective endocarditis: The Task Force for the Management of Infective Endocarditis of the European Society of Cardiology (ESC) Endorsed by: European Association for Cardio-Thoracic Surgery (EACTS), the European Association of Nuclear Medicine (EANM) The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study The impact of intravascular ultrasound guidance during drug eluting stent implantation on angiographic outcomes Histopathologic validation of the intravascular ultrasound diagnosis of calcified coronary artery nodules

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.