ArticleScientific reports2022
Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Maternal risk factors associated with neonatal necrotizing enterocolitis: a meta-analysis and systematic review.BMC pediatrics · 2026Pooled it
- Distinct clinical patterns of preterm infants vulnerable to intestinal injury identified by unsupervised clustering.Pediatric research · 2026Article
- Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges.Pediatric research · 2026Review
- Dual swin transformer for assisting in the diagnosis and surgical prediction of necrotizing enterocolitis.Pediatric research · 2026Article
- Machine learning demonstrates normal fetal Doppler velocimetry associated with reduced risk of necrotizing enterocolitis among preterm infants with growth restriction.Journal of neonatal-perinatal medicine · 2026Article
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- Explainable Artificial Intelligence for Rehospitalization and Financial Burden of Fertile Women in Orthopedic Care.Healthcare (Basel, Switzerland) · 2026Article
- Serum LRG1 as a diagnostic marker of necrotizing enterocolitis in preterm infants.Frontiers in pediatrics · 2026Article
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- [Recent advances in artificial intelligence for auxiliary diagnosis and management of neonatal necrotizing enterocolitis].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- A roadmap of artificial intelligence applications in pediatric surgery: a comprehensive review of applications, challenges, and ethical considerations.Pediatric surgery international · 2025Review
- The prediction models for the optimal timing of surgical intervention for necrotizing enterocolitis: nomogram vs. five machine learning models.Pediatric surgery international · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study.Children (Basel, Switzerland) · 2025Article
- Advances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis: A State-of-the-Art Review.Children (Basel, Switzerland) · 2025Review
- Clinical characterization of necrotizing enterocolitis in neonates with or without congenital heart disease: a case-control study.Italian journal of pediatrics · 2025Article
- Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.Psychiatry investigation · 2024Article
- Observational
- Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning.BMC medical informatics and decision making · 2024Article
- Explainable artificial intelligence on safe balance and its major determinants in stroke patients.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
This study used machine learning and a national prospective cohort registry database to analyze the major risk factors of necrotizing enterocolitis (NEC) in very low birth weight (VLBW) infants, including environmental factors. The data consisted of 10,353 VLBW infants from the Korean Neonatal Network database from January 2013 to December 2017. The dependent variable was NEC. Seventy-four predictors, including ambient temperature and particulate matter, were included. An artificial neural network, decision tree, logistic regression, naïve Bayes, random forest, and support vector machine were used to evaluate the major predictors of NEC. Among the six prediction models, logistic regression and random forest had the best performance (accuracy: 0.93 and 0.93, area under the receiver-operating-characteristic curve: 0.73 and 0.72, respectively). According to random forest variable importance, major predictors of NEC were birth weight, birth weight Z-score, maternal age, gestational age, average birth year temperature, birth year, minimum birth year temperature, maximum birth year temperature, sepsis, and male sex. To the best of our knowledge, the performance of random forest in this study was among the highest in this line of research. NEC is strongly associated with ambient birth year temperature, as well as maternal and neonatal predictors.
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