A Deep Learning Approach for the Estimation of Glomerular Filtration Rate-Reference-Cited by-同舟云学术

A Deep Learning Approach for the Estimation of Glomerular Filtration Rate

Published:2022-10 Issue:4 Volume:21 Page:560-569
ISSN:1536-1241
Container-title:IEEE Transactions on NanoBioscience
language:
Short-container-title:IEEE Trans.on Nanobioscience

Author:

Wang Haishuai¹^ORCID,Bowe Benjamin²,Cui Zhicheng³,Yang Hong⁴,Swamidass S. Joshua⁵,Xie Yan²,Al-Aly Ziyad²^ORCID

Affiliation:

1. College of Computer Science, Zhejiang University, Hangzhou, China

2. Clinical Epidemiology Center, Research and Education Service, VA St. Louis Health Care System, St. Louis, MO, USA

3. Google AI, Mountain View, CA, USA

4. Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

5. Institute for Informatics, Washington University in Saint Louis, St. Louis, MO, USA

Funder

Zhejiang Provincial Key Research and Development Program of China

Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Pharmaceutical Science,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/7728/9903485/09698014.pdf?arnumber=9698014

Reference58 articles.

1. Performance of Cystatin C– and Creatinine-Based Estimated Glomerular Filtration Rate Equations Depends on Patient Characteristics

2. Performance of MDRD study and CKD-EPI equations for long-term follow-up of nondiabetic patients with chronic kidney disease

3. Using Serum Creatinine To Estimate Glomerular Filtration Rate: Accuracy in Good Health and in Chronic Kidney Disease

4. Improving precision of glomerular filtration rate estimating model by ensemble learning

5. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond

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