Testing the use of local large language models to extract trauma identification and contextualize posttraumatic...

Accurate contextualization of trauma is critical for assessing posttraumatic stress disorder (PTSD) in behavioral health contexts, yet standard self-reports often fail to link symptoms to specific index traumas. This study evaluated the feasibility of using a local, privacy-preserving Large Language Model (LLM) to extract trauma exposure types from free-text narratives and evaluate their association with PTSD symptoms for description and prediction. Participants (N = 109) recruited online via Prolific as part of a larger study completed an extended Life Events Checklist (LEC) with up to three free-text trauma descriptions, and the PTSD Checklist for DSM-5 (PCL-5). A local LLM was prompted to extract trauma types, which were compared with trauma categorizations generated by two expert clinical psychologists. Results indicated variable agreement; the LLM demonstrated high specificity (> 85% for most trauma types) and strong agreement for more frequently reported experiences, such as sexual assault (κ = 0.68-0.74). Cluster analysis based on LLM-derived features revealed significant differences in total PCL-5 scores (p = .04) across clusters. Findings suggest local LLMs can effectively extract clinically relevant features from brief trauma descriptions. While refinement is needed, this approach offers preliminary evidence for a scalable and secure method to augment PTSD screening in behavioral medicine.