A synthetic-data pipeline and fine-tuned LLM system, EmoScan, screens for depressive and anxiety disorders from generated clinical interviews, outperforming GPT-4 on synthetic test data but only slightly on an external dataset.
LLM Questionnaire Completion for Automatic Psychiatric Assessment
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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.
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cs.CL 1years
2025 1verdicts
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Enhanced Large Language Models for Effective Screening of Depression and Anxiety
A synthetic-data pipeline and fine-tuned LLM system, EmoScan, screens for depressive and anxiety disorders from generated clinical interviews, outperforming GPT-4 on synthetic test data but only slightly on an external dataset.