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[2510.25232] From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
Summary
This article presents a novel approach to psychiatric comorbidity through the creation of a large-scale dataset and a multi-agent diagnostic dialogue system, enhancing diagnostic accuracy and treatment planning.
Why It Matters
Psychiatric comorbidity is a complex issue in mental health, often leading to misdiagnosis and ineffective treatment. This research provides a significant resource for improving diagnostic processes and developing AI models that can handle multiple disorders in a single conversation, potentially transforming psychiatric care.
Key Takeaways
- Introduces PsyCoTalk, a dataset with 3,000 validated diagnostic dialogues for psychiatric comorbidity.
- Employs a multi-agent framework to simulate clinical interviews, enhancing diagnostic accuracy.
- Demonstrates high fidelity in dialogue structure and reasoning compared to real-world transcripts.
- Supports the development of AI models for efficient multi-disorder psychiatric screening.
- Validated by licensed psychiatrists, ensuring clinical relevance and applicability.
Computer Science > Artificial Intelligence arXiv:2510.25232 (cs) [Submitted on 29 Oct 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity Authors:Tianxi Wan, Jiaming Luo, Siyuan Chen, Kunyao Lan, Jianhua Chen, Haiyang Geng, Mengyue Wu View a PDF of the paper titled From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity, by Tianxi Wan and 6 other authors View PDF HTML (experimental) Abstract:Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, we construct PsyCoTalk, the first large-scale dialogue dataset supporting comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric ...