Machine learning prediction of feeding intolerance in preterm infants: a pre-feeding risk stratification model.

ABSTRACT:

Background

Feeding intolerance (FI) represents a prevalent and serious complication in preterm infants, contributing to delayed enteral nutrition, prolonged hospitalization, and increased morbidity. Early identification of high-risk infants remains challenging due to limited predictive tools available before feeding initiation.

Methods

We conducted a retrospective cohort study of 402 preterm infants (<37 weeks gestational age) admitted between January 2023 and May 2024. Clinical data collected at admission underwent feature selection using cross-validated LASSO regression. Eleven machine learning algorithms were systematically compared using accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Clinical utility was assessed thr

SUBMITTER: Mao G 

PROVIDER: S-EPMC12446353 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature