让医疗对话系统自动生成精准追问问题，减少医生沟通负担。
Follow-up Question Generation For Enhanced Patient-Provider Conversations
- 多智能体框架融合患者语句与电子病历生成个性化追问。
- 实测可减少34%医生后续沟通量，真实数据性能提升17%。
- 开源首个带病历关联的异步医患对话数据集，适合医疗NLP研究。
追问问题生成是对话系统的关键功能，能降低对话歧义并增强复杂交互建模。医疗对话中常面临两大核心NLP挑战：(i) 从碎片化数据源中提取相关医学信息；(ii) 建模并行的诊疗推理过程。这些挑战在异步医患对话中尤为突出，因医生只能依赖静态电子病历（EHR）来提出后续问题。为此，本文提出FollowupQ，一个用于增强异步医疗对话的多智能体框架。该框架整合患者消息与电子病历，生成个性化的跟进问题，以澄清患者描述的病情。实验表明，FollowupQ可使医生所需后续沟通减少34%，在真实数据上性能提升17%，在合成数据上提升5%。同时，本文发布了首个公开的异步医疗对话数据集，包含2,300条由临床专家撰写的追问问题及其关联的电子病历，供更广泛的NLP研究社区使用。
原文摘要 · Abstract (English)
Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. Conversational contexts often pose core NLP challenges such as (i) extracting relevant information buried in fragmented data sources, and (ii) modeling parallel thought processes. These two challenges occur frequently in medical dialogue as a doctor asks questions based not only on patient utterances but also their prior EHR data and current diagnostic hypotheses. Asking medical questions in asynchronous conversations compounds these issues as doctors can only rely on static EHR information to motivate follow-up questions. To address these challenges, we introduce FollowupQ, a novel framework for enhancing asynchronous medical conversation. FollowupQ is a multi-agent framework that processes patient messages and EHR data to generate personalized follow-up questions, clarifying patient-reported medical conditions. FollowupQ reduces requisite provider follow-up communications by 34%. It also improves performance by 17% and 5% on real and synthetic data, respectively. We also release the first public dataset of asynchronous medical messages with linked EHR data alongside 2,300 follow-up questions written by clinical experts for the wider NLP research community.
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