Topic Modeling

Our presentation, Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling, was delivered by Dr. Shockley McCarthy at the AI for BI Conference at The Ohio State University on October 17, 2025.

Automated detection of IPV injury mechanisms from emergency department narratives using Large Language Models and Latent Dirichlet Allocation

This study applies machine learning and natural language processing to analyze child fatality reports in Pennsylvania from 2016 to 2023. Unstructured text narratives from child death investigations were combined with census data to identify recurring themes and environmental factors surrounding child neglect and traumatic injuries. Using structured topic modeling, the researchers identified 11 distinct circumstances of death, including substance misuse, sleep-related deaths, firearms, and supervisory neglect. The analysis showed that counties with higher poverty levels and larger non-White populations experienced a higher prevalence of firearm-related and severe traumatic injuries. The findings demonstrate how combining clinical narratives with machine learning can strengthen fatality surveillance and help construct more effective, targeted prevention programs.