Pediatric Firearm Risk Prediction in Trauma Centers and After Discharge: A Machine Learning Analysis

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Article · Mar 2026 · 1 min read

In brief

This study applies machine learning algorithms to predict firearm injury risk and post-discharge mortality in pediatric and adolescent trauma patients across U.S. trauma centers. The models aim to identify high-risk youth for targeted intervention and improve outcomes for firearm injury survivors through data-driven risk stratification.

Machine learning models can predict firearm injury risk in pediatric trauma patients at admission
Post-discharge mortality prediction is feasible for pediatric firearm injury survivors
Risk stratification tools may enable targeted intervention for high-risk youth in trauma settings
Data-driven approaches can identify children at elevated risk for firearm-related outcomes

Written by the GCMD Library team from the article.

We used machine learning (ML) to develop firearm risk prediction models for injured children and adolescents admitted to U.S. trauma centers, including prediction of death after discharge among survivors of firearm injury.

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