构建首个支持共病的临床对话数据集，助力精神疾病多病种筛查。
From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
- 用合成病历生成与多智能体对话框架，模拟真实问诊流程。
- 创建含3000轮对话的PsyCoTalk数据集，覆盖130+诊断状态。
- 经精神科医生验证，对话结构与推理策略高度贴近真实临床。
精神科共病临床意义重大但复杂难解。为此，我们提出一种结合合成电子病历（EMR）构建与多智能体诊断对话生成的新方法。通过管道化流程，构建了502份具有临床相关性与多样性的合成患者病历。多智能体框架将临床问诊协议转化为层次化状态机与上下文树，支持超过130个诊断状态，保持临床规范。经过严格流程，我们构建了首个大规模共病支持对话数据集PsyCoTalk，包含3000轮经精神科医生验证的多轮诊断对话。该数据集在对话长度、词元分布及诊断推理策略上表现出与真实临床转录稿相当的结构与语言保真度。精神科医生确认其真实性和诊断有效性。该数据集可支持单次对话中多疾病筛查模型的开发与评估。
原文摘要 · Abstract (English)
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 comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening in a single conversational pass.
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