Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration
Journal:
arXiv
Published Date:
Feb 16, 2025
Abstract
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.
