ArticleBMJ open2024
Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Interpretable machine learning-based predictive model for malnutrition in subacute post-stroke patients: an internal and external validation study.Frontiers in nutrition · 2025Article
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Authors and funding
7 authors.
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Abstract
backgroundPatients 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.
methodThis 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 occurs on the day of admission, at 3 and 7 days post-admission, including biochemical analysis, body composition indicators, disease severity scoring, nutritional risk screening, malnutrition diagnosis and nutritional support information. The occurrence rate of malnutrition in patients with acute abdomen is analysed with the GLIM criteria based on the Nutritional Risk Screening 2002 and the Mini Nutritional Assessment Short-Form to investigate the sensitivity and accuracy of the GLIM criteria. After data cleansing and preprocessing, a machine learning approach is employed to establish a predictive model for malnutrition diagnosis in patients with acute abdomen based on the GLIM criteria. ETHICS AND DISSEMINATION: This study has obtained ethical approval from the Ethics Committee of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital on 28 November 2022 (Yan-2022-442). The results of this study will be disseminated in peer-reviewed journals, at scientific conferences and directly to study participants. TRIAL REGISTRATION NUMBER: ChiCTR2200067044.
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