Kavli Affiliate: Zhuo Li
| First 5 Authors: Jinpeng Hu, Jinpeng Hu, , ,
| Summary:
Mental health assessment is crucial for early intervention and effective
treatment, yet traditional clinician-based approaches are limited by the
shortage of qualified professionals. Recent advances in artificial intelligence
have sparked growing interest in automated psychological assessment, yet most
existing approaches are constrained by their reliance on static text analysis,
limiting their ability to capture deeper and more informative insights that
emerge through dynamic interaction and iterative questioning. Therefore, in
this paper, we propose a multi-agent framework for mental health evaluation
that simulates clinical doctor-patient dialogues, with specialized agents
assigned to questioning, adequacy evaluation, scoring, and updating. We
introduce an adaptive questioning mechanism in which an evaluation agent
assesses the adequacy of user responses to determine the necessity of
generating targeted follow-up queries to address ambiguity and missing
information. Additionally, we employ a tree-structured memory in which the root
node encodes the user’s basic information, while child nodes (e.g., topic and
statement) organize key information according to distinct symptom categories
and interaction turns. This memory is dynamically updated throughout the
interaction to reduce redundant questioning and further enhance the information
extraction and contextual tracking capabilities. Experimental results on the
DAIC-WOZ dataset illustrate the effectiveness of our proposed method, which
achieves better performance than existing approaches.
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