Depression Detection in Clinical Interviews with LLM -Empowered Structural Element Graph (2024)

Abstract: Depression is a widespread mental health disorder affecting millions globally. Clinical interviews are the gold standard for assessing depression, but they heavily rely on scarce professional clinicians, highlighting the need for automated detection systems. However, existing methods only capture part of the relevant elements in clinical interviews, unable to incorporate all depressive cues. Moreover, the scarcity of participant data, due to privacy concerns and collection challenges, intrinsically constrains interview modeling. To address these limitations, in this paper, we propose a structural element graph (SEGA), which transforms the clinical interview into an expertise-inspired directed acyclic graph for comprehensive modeling. Additionally, we further empower SEGA by devising novel principle-guided data augmentation with large language models (LLMs) to supplement high-quality synthetic data and enable graph contrastive learning. Extensive evaluations on two real-world clinical datasets, in both English and Chinese, show that SEGA significantly outperforms baseline methods and powerful LLMs like GPT-3.5 and GPT-4.

## introduction depression is a pervasive mental health disorder that affects a significant portion of the global population. according to the world health organization (who) 1 , over 300 million people suffer from depression, casting a profound shadow on individuals, families, and society. furthermore, in many communities, limited awareness and stigmatization surrounding mental health can lead to under-diagnosis and under-treatment, which underscores the imperative for effective depression screening. ## conclusion in this paper, we propose a novel structural element graph sega for depression detection in clinical interviews. sega comprehensively captures all key elements -interview questions, answer transcripts, audios, and videos -within a simple yet effective directed acyclic graph. to further address data scarcity, we design the empowered sega ++ via principle-guided augmentation with llms and graph contrastive learning. experiments on two real-world datasets demonstrate the state-of-the-art performance of our method, surpassing the latest baseline methods and zero/few-shot gpt-3.5/4.