Machine Learning-Based Prediction of Surgical Intervention in Preterm Infants with Necrotizing Enterocolitis: A Retrospective Cohort Study.

ABSTRACT: Background: Necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disorder in neonates, particularly preterm infants. Early identification of infants requiring surgical intervention remains challenging due to nonspecific clinical manifestations and rapid disease progression. Methods: We conducted a retrospective cohort study of 320 preterm infants with NEC (gestational age <37 weeks) who were admitted to the NICU of the Capital Center for Children's Health, Capital Medical University, Beijing, China, between June 2017 and December 2024. Forty-three clinical, laboratory, and imaging variables were collected. Feature selection was performed using LASSO regression and the Boruta algorithm. Four machine learning (ML) models-LightGBM, XGBoost, Random Forest, and N

SUBMITTER: Li Y 

PROVIDER: S-EPMC12839886 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature