FANS: A framework for automatic assessment of nutritional status based on free-text clinical notes

作者：Jiahui Hu, Xue Wang, Kuanda Yao, Xu Zhang, Wanqing Zhao, Fu Jin, Pei Lou, Wei Chen, An Fang · 发表于：International Journal of Medical Informatics · 年份：2025 · DOI：10.1016/j.ijmedinf.2025.106168 · 被引用次数：4 · 研究领域：Nutrition and Health in Aging、Dietetics, Nutrition, and Education、Nutritional Studies and Diet

BACKGROUND: The prevalence of malnutrition is common in hospitalized patients. Timely and efficient nutrition support can improve clinical outcomes and save medical care costs. However, the current assessment of nutritional status relies on the one-by-one bedside patient interview, which is labor-intensive and time-consuming. Machine learning has shown its promise in clinical decision support, but it is challenged by the limited labeled samples in the real clinical use scenario. OBJECTIVE: We aimed to develop and validate an approach that automatically identifies malnutrition risk factors from free-text clinical notes, and assess nutritional status of hospitalized patients rapidly under the situation of limited labeled data. METHODS: The clinical notes of 1,469 patients used in this study were collected from the Peking Union Medical College Hospital. 495 hospitalized patients were recruited on admission and 974 patients were retrospectively collected. The proposed Framework for automatic Assessment of Nutritional Status (FANS) consists of two components: a risk factor identification component and a nutritional status assessment component. For risk factor identification, the notes were annotated according to the criteria of the global leadership initiative on malnutrition (GLIM), the subjective global assessment (SGA), and the European society for clinical nutrition and metabolism (ESPEN) guidelines for nutrition screening 2002 (NRS2002). Six natural language processing models...