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/aaronlifenghan/HealthQuote.NL

翻译：隐喻及隐喻性语言在临床医生、患者及其家属间的医疗沟通中扮演重要角色。本研究聚焦于荷兰语癌症患者数据，通过两种数据源提取患者使用的隐喻：(1) 癌症患者叙事访谈数据；(2) 在线论坛数据，包括患者发布的帖子、评论及向专业人士的提问。我们通过探索思维链推理、少样本学习及自我提示等不同提示策略，研究当前先进的大语言模型在此任务上的表现。通过人机协同验证机制，我们对提取的隐喻进行人工校验，并将输出结果整合为名为 HealthQuote.NL 的语料库。我们相信提取的隐喻有助于改善患者照护，例如促进医患共同决策、优化医患沟通并提升患者健康素养，同时可为个性化诊疗路径的设计提供参考。相关提示词及资源已发布于 https://github.com/aaronlifenghan/HealthQuote.NL