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            <namePart type="given">Mohit</namePart>
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    <abstract>We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The LLM is prompted to answer these questionnaires by impersonating the interviewee. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is shown to enhance diagnostic accuracy compared to multiple baselines. It thus establishes a novel framework for interpreting unstructured psychological interviews, bridging the gap between narrative-driven and data-driven approaches for mental health assessment.</abstract>
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%0 Conference Proceedings
%T LLM Questionnaire Completion for Automatic Psychiatric Assessment
%A Rosenman, Gony
%A Hendler, Talma
%A Wolf, Lior
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F rosenman-etal-2024-llm
%X We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The LLM is prompted to answer these questionnaires by impersonating the interviewee. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is shown to enhance diagnostic accuracy compared to multiple baselines. It thus establishes a novel framework for interpreting unstructured psychological interviews, bridging the gap between narrative-driven and data-driven approaches for mental health assessment.
%U https://aclanthology.org/2024.findings-emnlp.23
%P 403-415

Markdown (Informal)

[LLM Questionnaire Completion for Automatic Psychiatric Assessment](https://aclanthology.org/2024.findings-emnlp.23) (Rosenman et al., Findings 2024)

• LLM Questionnaire Completion for Automatic Psychiatric Assessment (Rosenman et al., Findings 2024)

ACL

• Gony Rosenman, Talma Hendler, and Lior Wolf. 2024. LLM Questionnaire Completion for Automatic Psychiatric Assessment. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 403–415, Miami, Florida, USA. Association for Computational Linguistics.