Patient-generated symptom expressions are linguistically diverse, often deviating from standardized medical terminology. This paper introduces the Japanese Patient Phrase Bank (JPPB), the first automatically constructed phrase-level normalization resource for Japanese patient language. JPPB introduces an embedding-based soft labeling framework that transforms traditional one-to-one dictionary mappings into graded and ambiguity-aware associations. This framework represents a shift from word-level to phrase-level normalization in Japanese. The resource covers 7,035 phrase–term pairs across 412 symptoms. Evaluation on the KEEPHA and MedNLP-SC datasets shows that soft labels consistently improve Top-1 accuracy and better approximate gold label distributions compared with hard labels. While LLM-based normalization achieved the highest scores, JPPB provides a lightweight and transparent alternative suitable for local deployment. This work demonstrates that large-scale, automatically generated phrase banks can achieve competitive performance relative to manually curated resources and serve as practical, scalable resources for medical natural language processing in Japanese.

Medical research is highly specialized, making it difficult for patients and general readers to understand recent findings.Traditionally, text simplification, replacing technical terms with more accessible expressions, has been employed. However, this approach alone is limited in addressing a lack of background knowledge and often results in the loss of important information.Therefore, this study defines “rewriting for non-experts” as a rewriting process that, in addition to simplification, supplements essential background knowledge such as the significance of the research and reasons it is needed and proposes a method for implementing this process using large language models (LLMs).To verify the effectiveness of the proposed approach, a quantitative evaluation using automatic metrics was conducted. The results showed that the method combining the guidelines for human text creation with few-shot examples of reference texts achieved the highest scores.The expansion of the guidelines is planned as part of future work to enable the rewriting of scientific and technological information in a form that is accessible to a broader audience.