Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis-Reference-Cited by-同舟云学术

Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis

Published:2023-07-28 Issue:2 Volume:55 Page:
ISSN:0785-3890
Container-title:Annals of Medicine
language:en
Short-container-title:Annals of Medicine

Author:

Bailey Alainn¹,Eltawil Mohamed²,Gohel Suril²,Byham-Gray Laura¹

Affiliation:

1. Department of Clinical and Preventive Nutrition Sciences, School of Health Professions, Rutgers University, New Brunswick, NJ, USA

2. Department of Health Informatics, School of Health Professions, Rutgers University, New Brunswick, NJ, USA

Funder

National Institute of Health

AHRQ

Academy of Nutrition and Dietetics

Rutgers Intramural School of Health Professions Grant Program

Publisher

Informa UK Limited

Subject

General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/07853890.2023.2238182

Reference53 articles.

1. United States Renal Data System. Annual data report, executive summary. 2019 [cited 2023 May 20]. Available from: https://www.usrds.org/media/2371/2019-executive-summary.pdf

2. National Kidney Foundation. KDOQI. clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. 2002 [cited 2023 May 20]. Available from: https://www.kidney.org/sites/default/files/docs/ckd_evaluation_classification_stratification.pdf. Accessed May 20 2023.

3. Energy Expenditure in People with Diabetes Mellitus: A Review

4. A proposed nomenclature and diagnostic criteria for protein–energy wasting in acute and chronic kidney disease

5. Etiology of the Protein-Energy Wasting Syndrome in Chronic Kidney Disease: A Consensus Statement From the International Society of Renal Nutrition and Metabolism (ISRNM)