@inproceedings{c455467eda5349b59458d874f7e554e0,
title = "LLM Questionnaire Completion for Automatic Psychiatric Assessment",
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 answers obtained 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. Thus, it establishes a novel framework for interpreting unstructured psychological interviews, bridging the gap between narrative-driven and data-driven approaches for mental health assessment.",
author = "Gony Rosenman and Lior Wolf and Talma Hendler",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; 2024 Findings of the Association for Computational Linguistics, EMNLP 2024 ; Conference date: 12-11-2024 Through 16-11-2024",
year = "2024",
doi = "10.18653/v1/2024.findings-emnlp.23",
language = "الإنجليزيّة",
series = "EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024",
publisher = "Association for Computational Linguistics (ACL)",
pages = "403--415",
editor = "Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen",
booktitle = "EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024",
address = "الولايات المتّحدة",
}