Machine learning-based prediction of mortality in pediatric trauma patients.

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% (n = 8,250). Most subjects were male (67%, n = 342,571) and w

SUBMITTER: Deleon A 

PROVIDER: S-EPMC11905922 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature