ArticleInternational journal of environmental research and public health2018
Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Pooled it
- 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
- [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
- 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
- A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study.European journal of pediatrics · 2025Observational
- Review
- AXpert: human expert facilitated privacy-preserving large language models for abdominal X-ray report labeling.JAMIA open · 2025Article
- 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
- Artificial intelligence to classify acquired intestinal injury in preterm neonates-a new perspective.Pediatric research · 2024Article
- Nutritional Strategies for Preterm Neonates and Preterm Neonates Undergoing Surgery: New Insights for Practice and Wrong Beliefs to Uproot.Nutrients · 2024Review
- Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation.European journal of pediatrics · 2024Article
- Understanding necrotizing enterocolitis endotypes and acquired intestinal injury phenotypes from a historical and artificial intelligence perspective.Frontiers in pediatrics · 2024Review
- [Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023Article
- Development of an international core outcome set for treatment trials in necrotizing enterocolitis-a study protocol.Trials · 2023Article
- Biomarkers of necrotizing enterocolitis in the era of machine learning and omics.Seminars in perinatology · 2023Review
- Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns.Pediatric research · 2023Review
- State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis.Frontiers in pediatrics · 2023Review
- Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors.Healthcare (Basel, Switzerland) · 2022Article
- A critical evaluation of current definitions of necrotizing enterocolitis.Pediatric research · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were
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