Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration
This work addresses depression screening for clinicians by enabling interactive theme-based analysis, though it is incremental as it builds on existing neural network approaches with specific enhancements.
The paper tackled the problem of automatic depression detection from clinical interview dialogues by addressing defects in existing methods that fail to capture intra-theme and inter-theme correlations and lack clinician interactivity, resulting in absolute improvements of 35% and 12% compared to state-of-the-art on the DAIC-WOZ dataset.
Automatic depression detection provides cues for early clinical intervention by clinicians. Clinical interviews for depression detection involve dialogues centered around multiple themes. Existing studies primarily design end-to-end neural network models to capture the hierarchical structure of clinical interview dialogues. However, these methods exhibit defects in modeling the thematic content of clinical interviews: 1) they fail to capture intra-theme and inter-theme correlation explicitly, and 2) they do not allow clinicians to intervene and focus on themes of interest. To address these issues, this paper introduces an interactive depression detection framework. This framework leverages in-context learning techniques to identify themes in clinical interviews and then models both intra-theme and inter-theme correlation. Additionally, it employs AI-driven feedback to simulate the interests of clinicians, enabling interactive adjustment of theme importance. PDIMC achieves absolute improvements of 35\% and 12\% compared to the state-of-the-art on the depression detection dataset DAIC-WOZ, which demonstrates the effectiveness of modeling theme correlation and incorporating interactive external feedback.