ArticleScientific reports2024
Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- From oxidative stress to epigenetic regulation: Molecular mechanisms of preterm brain injury and neuroprotective strategies (Review).Molecular medicine reports · 2026Review
- 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
- Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.BMC medical informatics and decision making · 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
- Development of a machine learning-based multicenter study for predicting severe intraventricular hemorrhage in preterm infants using perinatal variables.Frontiers in neurology · 2026Observational
- Deep learning approach for screening neonatal cerebral lesions on ultrasound in China.Nature communications · 2025Article
- Mitophagy in Brain Injuries: Mechanisms, Roles, and Therapeutic Potential.Molecular neurobiology · 2025Review
- AI-guided precision parenteral nutrition for neonatal intensive care units.Nature medicine · 2025Article
- Risk factors for intraventricular hemorrhage in very low birth weight infants: a systematic review and meta-analysis.Frontiers in pediatrics · 2025Review
- 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
- Prevalence and associated risk factors of intraventricular hemorrhage in preterm newborns in Southwestern Iran.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
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Authors and funding
10 authors.
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Abstract
Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (VLBW) preterm infants in Taiwan. We collected retrospective data from VLBW infants, dividing them into two cohorts: one for model development and internal validation (Cohort 1, 2016-2021), and another for external validation (Cohort 2, 2022). Primary outcomes included early mortality, severe IVH, and early poor outcomes (a combination of both). Data preprocessing involved 23 variables, with the top four predictors identified as gestational age, birth body weight, 5-min Apgar score, and endotracheal tube ventilation. Six machine learning algorithms were employed. Among 7471 infants analyzed, the selected predictors consistently performed well across all outcomes. Logistic regression and neural network models showed the highest predictive performance (AUC 0.81-0.90 in both internal and external validation) and were well-calibrated, confirmed by calibration plots and the lowest two mean Brier scores (0.0685 and 0.0691). We developed a robust machine learning-based outcome predictor using only four accessible variables, offering valuable prognostic information for parents and aiding healthcare providers in decision-making.
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