Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters

Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters

IntroductionDisease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the curre...

Full description

Saved in:
Bibliographic Details
Main Authors: Karen Rischmüller, Vanessa Caton, Markus Wolfien, Luise Ehlers, Matti van Welzen, David Brauer, Lea F. Sautter, Fatuma Meyer, Luzia Valentini, Mats L. Wiese, Ali A. Aghdassi, Robert Jaster, Olaf Wolkenhauer, Georg Lamprecht, Saptarshi Bej
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Nutrition
Subjects:
malnutrition
GLIM criteria
machine learning
supervised and unsupervised learning
decision trees
gastrointestinal diseases
Online Access:https://www.frontiersin.org/articles/10.3389/fnut.2024.1479501/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.frontiersin.org/articles/10.3389/fnut.2024.1479501/full

Similar Items