Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025)

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.

## introduction depression is one of the most prevalent mental health disorders, affecting millions of individuals worldwide @xcite . timely detection is crucial for effective intervention, yet traditional assessment methods, such as clinical interviews, rely heavily on trained professionals, which are in short supply @xcite . as a result, there is an increasing need for automated systems capable of accurately detecting depression * corresponding author. from patient interactions, enabling faster and more widespread diagnosis @xcite @xcite . most automated depression detection methods currently focus on enhancing system performance using various approaches. previous work has concentrated on aggregating word representations for prediction @xcite , or further incorporating affective and mental health lexicons @xcite . some studies have explored modalities beyond text, such as audio @xcite , or have integrated multimodal data @xcite . however, in health-related tasks, precision is not the sole priority; it is also crucial to understand the rationale behind the system's predictions to make the system more transparent and reliable. to achieve this, some studies have utilized the internal states of neural models, such as attention scores @xcite , though these explanations often fall short of human interpretability. in response, some researchers have leveraged the generative capabilities of large language models (llms) to create post-hoc explanations based on the system's predictions @xcite . as shown in figure 1 , most previous work overlooks the interpretability of the system, making its predictions difficult to understand and less reliable @xcite . some studies have attempted to generate post-hoc explanations using large language models (llms), but these approaches are often plagued by the issue of hallucinated generation @xcite . to address these challenges, we propose employing the retrieval-augmented generation (rag) framework for explainable depression detection. the rag framework combines a retriever model with llms to improve the llm's ability to handle content beyond its input window and to update its knowledge @xcite . with the rag framework, crucial information from the interview dialogue is retrieved and serves as supporting evidence for the llm's prediction. furthermore, the retrieval process helps filter out noisy or irrelevant information that could negatively impact the prediction. since the evidence is directly retrieved from the interview text, it is both human-understandable and free from hallucinations. in this paper, we propose red, a retrievalaugmented generation framework for explainable depression detection. red retrieves relevant evidence from clinical interview transcripts and uses this information as an explanation for its predictions. this retrieval-based approach ensures that the explanations are grounded in the actual content of the interview, enhancing transparency. since depression detection is a personalized task where participants' backgrounds can vary, the traditional approach of using a single, unified query for all users may lead to suboptimal results. to address this, we introduce a personal query generation module in red, which tailors the basic query to each individual based on their profile, inferred from the llm. this customization enables more accurate and context-sensitive predictions. while llms have proven effective across many tasks due to their extensive world knowledge, they often lack domain-specific knowledge and fall short of social intelligence @xcite @xcite . to mitigate this, we enhance the social intelligence of llms by retrieving external knowledge from a social intelligence knowledge base using an event-centric retriever. experimental results on a real-world depression detection benchmark demonstrate that red outperforms both neural network-based and llm-based methods, highlighting its effectiveness. the contributions of this paper can be summarized as: ## experimental analysis in this section, we present comprehensive experiments conducted on lamp. through an in-depth analysis of the results, we aim to address the following research questions (rqs): ## conclusion in this paper, we introduced red, a retrieval-augmented generation framework designed for explainable depression detection. by retrieving evidence from clinical interview transcripts, red not only provides transparent explanations for its predictions but also adapts to individual user contexts through personalized query generation. furthermore, we enhanced the rag by retrieving social intelligence knowledge with an event-centric retriever. experimental results on the real-world dataset validate the effectiveness of red, demonstrating its effectiveness in explainable depression detection.