MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents
Congchi Yin, Feng Li, Shu Zhang, Zike Wang, Jun Shao, Piji Li, Jianhua, Chen, Xun Jiang

TL;DR
This paper introduces MDD-5k, the largest Chinese mental disorder diagnosis dataset created by a neuro-symbolic LLM framework that synthesizes diagnostic conversations from anonymized patient cases, enabling AI research in mental health.
Contribution
It presents a novel neuro-symbolic multi-agent framework for synthesizing diagnostic conversations, resulting in the first large-scale Chinese mental disorder dataset with labels.
Findings
The dataset contains 5000 high-quality conversations.
Human evaluation confirms the dataset's realism.
First labeled Chinese mental disorder diagnosis dataset.
Abstract
The clinical diagnosis of most mental disorders primarily relies on the conversations between psychiatrist and patient. The creation of such diagnostic conversation datasets is promising to boost the AI mental healthcare community. However, directly collecting the conversations in real diagnosis scenarios is near impossible due to stringent privacy and ethical considerations. To address this issue, we seek to synthesize diagnostic conversation by exploiting anonymized patient cases that are easier to access. Specifically, we design a neuro-symbolic multi-agent framework for synthesizing the diagnostic conversation of mental disorders with large language models. It takes patient case as input and is capable of generating multiple diverse conversations with one single patient case. The framework basically involves the interaction between a doctor agent and a patient agent, and generates…
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Taxonomy
TopicsMachine Learning in Healthcare · Mental Health Research Topics · Mental Health via Writing
