Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

ABSTRACT:

Objective

Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.

Materials and methods

We used clinical notes from 36 685 Veterans Health Administration (VHA) patients between April 10, 2023 and April 10, 2024. We expanded preexisting firearm term sets using subject matter experts and generated 250-character snippets around each firearm term appearing in notes. Annotators labeled 3000 snippets into three classes. Using these annotated snippets, we compared four nonneural machine learning models (random forest, bagging, gradient boosting, logistic regression with ridge penalization) and two versions of Bidirectional Encoder Representations from Transforme

SUBMITTER: Trujeque J 

PROVIDER: S-EPMC11648724 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature