Supervised Natural Language Processing Classification of Violent Death Narratives: Development and Assessment of a Compact Large Language Model.

ABSTRACT: The recent availability of law enforcement and coroner or medical examiner reports for nearly every violent death in the United States expands the potential for natural language processing (NLP) research into violence. The objective of this work is to assess applications of supervised NLP to unstructured data in the National Violent Death Reporting System to predict circumstances and types of violent death. This analysis applied distilBERT, a compact large language model (LLM) with fewer parameters relative to full-scale LLMs, to unstructured narrative data to simulate the impacts of preprocessing, volume, and composition of training data on model performance, evaluated by F1-scores, precision, recall, and the false negative rate. Model performance was evaluated for bias by race, ethnicity

SUBMITTER: Parker ST 

PROVIDER: S-EPMC12223685 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature