Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol.

ABSTRACT:

Background

Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional diagnosis. Additionally, machine learning is emerging to establish artificial intelligent-enabled diagnostic models, but the accuracy and robustness need to be verified. We aimed to establish an intelligence-enabled malnutrition diagnosis model based on GLIM for patients with acute abdomen.

Method

This study is a single-centre, cross-sectional observational investigation into the prevalence of malnutrition in patients with acute abdomen using the GLIM criteria. Data collection occur

SUBMITTER: Ma W 

PROVIDER: S-EPMC10928796 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature