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

ACL ARR 2025 February Submission6159 Authors

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: healthcare applications, retrieval-augmented generation, interpretability

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 6159