用于优化医患对话的追问生成
Follow-up Question Generation For Enhanced Patient-Provider Conversations
AI总结：
针对异步医疗对话中追问生成的信息提取与并行思维建模难题，提出多智能体框架FollowupQ，可减少医护沟通量并提升性能，还发布了配套的公开数据集。
摘要：
追问生成是对话系统的一项核心功能，它能够减少对话歧义并强化对复杂交互的建模。对话语境通常会带来核心自然语言处理（NLP）挑战，例如（i）提取隐藏在碎片化数据源中的相关信息，以及（ii）对并行思维过程进行建模。这两类挑战在医疗对话中频繁出现，因为医生不仅要根据患者的表述提问，还要结合患者既往的电子健康记录（EHR）数据和当前的诊断假设。在异步对话中提出医疗问题会让这些问题更加棘手，因为医生只能依靠静态的EHR信息来生成追问。\n为应对这些挑战，我们提出了FollowupQ——一种用于优化异步医疗对话的新型框架。FollowupQ是一个多智能体框架，它处理患者消息和EHR数据，生成个性化的追问，以澄清患者报告的病情。FollowupQ将医护人员所需的后续沟通量减少了34%；在真实数据和合成数据上，其性能分别提升了17%和5%。我们还发布了首个关联EHR数据的异步医疗消息公开数据集，附带2300个由临床专家撰写的追问，供更广泛的NLP研究社区使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
