ArticleFrontiers in pediatrics2025
Machine learning-based prediction of mortality in pediatric trauma patients.
Article in Frontiers in pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Development and validation of a prediction model for respiratory complications and in-hospital mortality in trauma patients.Scientific reports · 2026Article
- Machine learning-based mortality prediction models for emergency department patients: a comparative analysis.Frontiers in medicine · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to develop a predictive model for mortality outcomes among pediatric trauma patients using machine learning (ML) algorithms. Methods: We extracted data on a cohort of pediatric trauma patients (18 years and younger) from the National Trauma Data Bank (NTDB). The main aim was to identify clinical and physiologic variables that could serve as predictors for pediatric trauma mortality. Data was split into a development cohort (70%) to build four ML models and then tested in a validation cohort (30%). The area under the receiver operating characteristic curve (AUC) was used to assess each model's performance. Results: In 510,381 children, the gross mortality rate was 1.6% ( Conclusion: Machine learning algorithms can be effectively utilized to build an accurate pediatric mortality prediction model that leverages variables easily obtained upon trauma admission.
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