Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2503.01315v1
• Date: Mon, 03 Mar 2025 08:59:34 GMT
• Title: Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation
• Authors: Linhai Zhang, Ziyang Gao, Deyu Zhou, Yulan He,
• Abstract summary: Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment.<n>Current systems mainly employ black-box neural networks, which lack interpretability.<n>We propose RED, a Retrieval-augmented generation framework for Explainable depression Detection.
• Score: 32.163466666512996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce professionals highlights the need for automated detection. Current systems mainly employ black-box neural networks, which lack interpretability, which is crucial in mental health contexts. Some attempts to improve interpretability use post-hoc LLM generation but suffer from hallucination. To address these limitations, we propose RED, a Retrieval-augmented generation framework for Explainable depression Detection. RED retrieves evidence from clinical interview transcripts, providing explanations for predictions. Traditional query-based retrieval systems use a one-size-fits-all approach, which may not be optimal for depression detection, as user backgrounds and situations vary. We introduce a personalized query generation module that combines standard queries with user-specific background inferred by LLMs, tailoring retrieval to individual contexts. Additionally, to enhance LLM performance in social intelligence, we augment LLMs by retrieving relevant knowledge from a social intelligence datastore using an event-centric retriever. Experimental results on the real-world benchmark demonstrate RED's effectiveness compared to neural networks and LLM-based baselines.

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