ArticlePloS one2022
Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Methods of identifying surgical Necrotizing Enterocolitis-a systematic review and meta-analysis.Pediatric research · 2025Pooled it
- 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
- AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.Pediatric research · 2025Review
- Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study.Forensic science, medicine, and pathology · 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
- Diagnostic and Prognostic Value of Hematological Parameters in Necrotizing Enterocolitis: A Systematic Review.Journal of clinical medicine · 2025Review
- Use of CatBoost algorithm to identify the need for surgery in infants with necrotizing enterocolitis.Frontiers in pediatrics · 2025Article
- Data-independent acquisition-based blood proteomics unveils predictive biomarkers for neonatal necrotizing enterocolitis.Analytical and bioanalytical chemistry · 2025Article
- Artificial intelligence to classify acquired intestinal injury in preterm neonates-a new perspective.Pediatric research · 2024Article
- Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.European journal of pediatrics · 2024Article
- [ROC curves: general characteristics and their usefulness in clinical practice].Revista medica del Instituto Mexicano del Seguro Social · 2023Article
- State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis.Frontiers in pediatrics · 2023Review
- Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia-red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.
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