Machine Learning-Based Prediction of Surgical Intervention in Preterm Infants with Necrotizing Enterocolitis: A Retrospective Cohort Study
Ying Li, Peipei Zhang, Jing Wu, Ying Wang, Ying Chen, Sihan Sheng, Yajuan Wang, Xiaohui Li · 2025
Read Machine Learning-Based Prediction of Surgical Intervention in Preterm Infants with Necrotizing Enterocolitis: A Retrospective Cohort Study by Ying Li, Peipei Zhang, Jing Wu, Ying Wang, Ying Chen, Sihan Sheng, Yajuan Wang, Xiaohui Li (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12839886.txt.
Description
Ying Li and seven co-authors built machine learning models to predict which preterm babies with gut disease need surgery, in a 2025 study. The disease, necrotizing enterocolitis, can worsen fast in fragile infants. Three hundred twenty babies at a Beijing center were studied. Forty-three clinical, lab, and imaging measures were collected. About a third of the babies had surgery.
Two selection methods narrowed the inputs to thirteen shared features. Four model types were trained and compared. A neural network scored best on overall accuracy. A random forest led on one ranking measure, and a boosting model on another. Inflammation levels, belly tenderness signs, and gestational age mattered most. Explanations came from a standard importance method. The authors offer the models as decision support. Earlier surgery calls could improve outcomes for these infants.
Catalog record
| CREATOR | Ying Li, Peipei Zhang, Jing Wu, Ying Wang, Ying Chen, Sihan Sheng, Yajuan Wang, Xiaohui Li |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |