AUTHOR=Yong-guang Shang , Xue-lian Wang , Yong Cheng , Wang-jun Qin , Peng-mei Li , Lei Zhang 

TITLE=Machine learning predicts lipid emulsion stability in parenteral nutrition using multi-laboratory literature data

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1668464

DOI=10.3389/fnut.2025.1668464

ISSN=2296-861X

ABSTRACT=ObjectivePhysical instability of lipid in parenteral nutrition (PN) poses significant clinical safety risks. As lipid stability is influenced by multiple complex factors and remains incompletely characterized, this study aimed to quantify the relative importance of stability determinants and to develop a machine learning (ML) model for predicting stability in individualized PN prescriptions.MethodsA retrospective meta-analysis integrated experimental data from multi-laboratory studies. The ML framework employed transfer learning for cross-laboratory data harmonization and Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance mitigation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) and accuracy.ResultsThe datasets comprised 17 stability-related features (electrolytes, macronutrients, and storage conditions) extracted from 1,518 samples representing 872 unique PN formulations across 19 studies (2000 and 2024). The XGBoost model achieved exceptional predictive performance (accuracy: 98.2%, AUC 0.968). SHAP-based feature importance analysis identified the concentrations of Amino and phosphate, storage time and lipid composition as key stability determinants.ConclusionThis study establishes the first interpretable ML framework for predicting lipid emulsions stability in PN, resolving cross-laboratory data heterogeneity. We have provided a high-accuracy prediction tool for assessing lipid emulsion stability in PN, while the methodology demonstrates generalizability for stability studies of complex drugs and nutrients formulations.