Abstract

Warning: This paper discusses topics of suicide and suicidal ideation, which may be distressing to some readers. The National Violent Death Reporting System (NVDRS) documents information about suicides in the United States, including free text narratives (eg, circumstances surrounding a suicide). In a demanding public health data pipeline, annotators manually extract structured information from death investigation records following extensive guidelines developed painstakingly by experts. In this work, we facilitate data-driven insights from the NVDRS data to support the development of novel suicide interventions by investigating the value of language models (LMs) as efficient assistants to these (a) data annotators and (b) experts. We find that LM predictions match existing data annotations about 85% of the time across 50 NVDRS variables. In the cases where the LM disagrees with existing annotations, expert …

Date

January 1, 1970

Authors

Jaspreet Ranjit, Hyundong J Cho, Claire J Smerdon, Yoonsoo Nam, Myles Phung, Jonathan May, John R Blosnich, Swabha Swayamdipta

Journal

arXiv e-prints

Pages

arXiv: 2508.18541