利用LLM和人机协同从癌症患者访谈和论坛数据中提取荷兰语隐喻
Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop
AI总结：
本研究利用LLM和人机协同方法，从癌症患者访谈和论坛数据中提取荷兰语隐喻，构建HealthQuote.NL语料库，以支持患者护理和个性化医疗路径设计
摘要：
隐喻和隐喻性语言（MLs）在临床医生、患者及其家属之间的医疗沟通中起着重要作用。在本工作中，我们专注于来自癌症患者的荷兰语数据。我们使用两种数据源提取患者使用的隐喻：（1）癌症患者讲故事的访谈数据和（2）在线论坛数据，包括患者帖子、评论和向专业人士提问的内容。我们研究了当前最先进的大型语言模型（LLMs）在该任务上的表现，通过探索不同的提示策略，如链式推理、少样本学习和自提示。通过人机协同设置，我们验证了提取的隐喻，并将输出编译成一个名为HealthQuote.NL的语料库。我们相信提取的隐喻可以支持更好的患者护理，例如共享决策、改善患者与临床医生之间的沟通以及提高患者健康素养。它们还可以用于个性化护理路径的设计。我们分享了提示和相关资源，网址为https://github.com/4dpicture/HealthQuote.NL
英文摘要：
Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients. We extract metaphors used by patients using two data sources: (1) cancer patient storytelling interview data and (2) online forum data, including patients' posts, comments, and questions to professionals. We investigate how current state-of-the-art large language models (LLMs) perform on this task by exploring different prompting strategies such as chain of thought reasoning, few-shot learning, and self-prompting. With a human-in-the-loop setup, we verify the extracted metaphors and compile the outputs into a corpus named HealthQuote.NL. We believe the extracted metaphors can support better patient care, for example shared decision making, improved communication between patients and clinicians, and enhanced patient health literacy. They can also inform the design of personalized care pathways. We share prompts and related resources at https://github.com/4dpicture/HealthQuote.NL
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。
