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

ABSTRACT:

Objective

Physical 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.

Methods

A 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.

SUBMITTER: Yong-Guang S 

PROVIDER: S-EPMC12667437 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature