Abstract
BACKGROUND: Firearm suicide has been more prevalent among males, but age-adjusted female firearm suicide rates increased by 20% from 2010 to 2020, outpacing the rate increase among males by about 8 percentage points, and female firearm suicide may have different contributing circumstances. In the United States, the National Violent Death Reporting System (NVDRS) is a comprehensive source of data on violent deaths and includes unstructured incident narrative reports from coroners or medical examiners and law enforcement. Conventional natural language processing approaches have been used to identify common circumstances preceding female firearm suicide deaths but failed to identify rarer circumstances due to insufficient training data. OBJECTIVE: This study aimed to leverage a large language model approach to identify infrequent circumstances preceding female firearm suicide in the unstructured coroners or medical examiners and law enforcement narrative reports available in the NVDRS. METHODS: We used the narrative reports of 1462 female firearm suicide decedents in the NVDRS from 2014 to 2018. The reports were written in English. We coded 9 infrequent circumstances preceding female firearm suicides. We experimented with predicting those circumstances by leveraging a large language model approach in a yes/no question-answer format. We measured the prediction accuracy with F(1)-score (ranging from 0 to 1). F(1)-score is the harmonic mean of precision (positive predictive value) and recall (true positive rate or sensitivity). RESULTS: Our large language model outperformed a conventional support vector machine-supervised machine learning approach by a wide margin. Compared to the support vector machine model, which had F(1)-scores less than 0.2 for most infrequent circumstances, our large language model approach achieved an F(1)-score of over 0.6 for 4 circumstances and 0.8 for 2 circumstances. CONCLUSIONS: The use of a large language model approach shows promise. Researchers interested in using natural language processing to identify infrequent circumstances in narrative report data may benefit from large language models.