aceMachine learning-based prediction of hospital-acquired malnutrition at discharge using the GLIM criteria: A multicenter real-world study
Objective This study aimed to investigate the incidence and clinical impact of hospital-acquired malnutrition (HAM) and develop a machine learning model for early risk prediction. Methods This secondary analysis included prospectively collected data from a multicenter observational study involving 34 tertiary hospitals in China. Adult patients hospitalized for 7 to 30 days were enrolled. Patients without malnutrition at admission were evaluated for HAM at discharge according to the Global Leadership Initiative o…
Among patients not malnourished on admission, about one in five developed malnutrition during hospitalization, and these patients had more complications, longer stays and higher costs.
Evidence
- Peer-reviewedNutrition2026-10-14
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Truvace Impact Record TRV-2026-1922, v1: “Machine learning-based prediction of hospital-acquired malnutrition at discharge using the GLIM criteria: A multicenter real-world study.” Truvace, 2026-10-19. /record/TRV-2026-1922 (accessed at citation time). sha256 c09f2f42ef507ec1…
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