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Open access Aug 2026

Development and validation of an interpretable machine learning model for predicting enteral nutrition-associated diarrhea in ICU patients: a multicenter study

The externally validated random forest model showed acceptable discriminative performance and provided an interpretable assessment of the contributions of individual features, which may support static early risk stratification of ENAD among critically ill patients receiving enteral nutrition.

Yu-Hong Wang, Wei Hu, Cai-Yue Xu et al. · 0 citations
Open access Aug 2026

An evaluation based on explainable machine learning: analysis of risk factors for enteral nutrition-related diarrhea in patients with severe stroke

The results suggest that, in addition to focusing on the patients' clinical condition and nutritional interventions themselves, clinicians should also pay attention to the regulation of ward temperature and the proper management of relevant medications.

Cai-Yue Xu, Wei Hu, Yu-Hong Wang et al. · 0 citations

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