AUTHOR=Liao Xiaoying , Li Chunhua , Liu Qunyan , Xia Wang , Liu Zhenglin , Zhu Jiamao , Hu Wei , Hong Qionghua 

TITLE=Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1584717

DOI=10.3389/fnut.2025.1584717

ISSN=2296-861X

ABSTRACT=BackgroundEnteral Nutrition-Associated Diarrhea (ENAD) is a common complication in critically ill patients, significantly impacting clinical outcomes. Accurately predicting the risk of ENAD is crucial for early intervention and improving patient care.ObjectiveThis study aims to develop and validate a machine learning (ML)-based risk prediction model for Enteral Nutrition-Associated Diarrhea (ENAD) in ICU patients, and explore its application in nursing practice.MethodThis study was conducted from January 2023 to October 2024 in the Comprehensive Intensive Care Unit (ICU) of a tertiary hospital in China, retrospectively analyzing data from ICU patients receiving enteral nutrition. LASSO regression was used for feature selection, and 9 machine learning (ML) algorithms were evaluated. Model performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanation (SHAP) method was employed to interpret feature importance and determine the final model.ResultsAmong the 9 ML models, the random forest (RF) model demonstrated the highest discriminative ability, achieving an AUC (95% CI) of 0.777 (0.702–0.830). After dimensionality reduction based on feature importance analysis, a simplified and interpretable RF model with 12 key predictors was established, yielding an AUC (95% CI) of 0.754 (0.685–0.823).ConclusionThe RF-based predictive model developed in this study provides a reliable and interpretable tool for identifying the risk of ENAD in ICU patients, contributing to targeted nursing interventions and improved patient outcomes. The research highlights the potential of machine learning in enhancing clinical decision-making and personalized care.