ArticleScientific reports2024
Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Developing and Validating a Machine Learning Model to Predict Brain Injury in Preterm Infants Using Multisource Data from the Early Postnatal Period.Children (Basel, Switzerland) · 2026Article
- Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol.Systematic reviews · 2026Article
- Predictive model for severe intraventricular hemorrhage risk in preterm infants: a systematic review and meta-analysis.Frontiers in pediatrics · 2026Review
- Deep learning approach for screening neonatal cerebral lesions on ultrasound in China.Nature communications · 2025Article
- Prediction models for intraventricular hemorrhage in very preterm infants: a systematic review.Frontiers in pediatrics · 2025Review
- Development and validation of a nomogram for predicting early-onset severe intraventricular hemorrhage in extremely preterm infants.Frontiers in pediatrics · 2025Article
- Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data.Children (Basel, Switzerland) · 2024Article
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3 authors.
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Abstract
Severe intraventricular hemorrhage (IVH) in premature infants can lead to serious neurological complications. This retrospective cohort study used the Korean Neonatal Network (KNN) dataset to develop prediction models for severe IVH or early death in very-low-birth-weight infants (VLBWIs) using machine-learning algorithms. The study included VLBWIs registered in the KNN database. The outcome was the diagnosis of IVH Grades 3-4 or death within one week of birth. Predictors were categorized into three groups based on their observed stage during the perinatal period. The dataset was divided into derivation and validation sets at an 8:2 ratio. Models were built using Logistic Regression with Ridge Regulation (LR), Random Forest, and eXtreme Gradient Boosting (XGB). Stage 1 models, based on predictors observed before birth, exhibited similar performance. Stage 2 models, based on predictors observed up to one hour after birth, showed improved performance in all models compared to Stage 1 models. Stage 3 models, based on predictors observed up to one week after birth, showed the best performance, particularly in the XGB model. Its integration into treatment and management protocols can potentially reduce the incidence of permanent brain injury caused by IVH during the early stages of birth.
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