Shido Noriyuki, Iwahashi Yuma, Ohsawa Hidenari, Furuya Katsushige, Sakai Yasumichi, Ishii Masamichi, Hoshimoto Hiroyuki, Namiki Nobukazu, Miyo Kengo
Mitsubishi Electric Software Corporation, Tokyo, Japan.
Center for Medical Informatics Intelligence, National Center for Global Health and Medicine, Tokyo, Japan.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:511-517. eCollection 2025.
There is an increasing demand for automatic mapping to standardized codes such as LOINC codes to create integrated medical databases across multiple facilities. However, natural language processing (NLP) in Japanese presents greater challenges than in English owing to a limited Japanese corpus for medical terms, such as test names. To address this limitation, we developed a machine learning-based method that maps in-house codes to LOINC codes by leveraging test result values without relying on test names that would require NLP. Using this approach, we achieved high mapping accuracy (70% or higher) for 80.4% of the analytes targeted in this study. The proposed method facilitates easier mapping to standardized codes in languages where NLP is challenging, ensuring accurate mapping to LOINC codes regardless of the source data language.
对自动映射到标准化代码（如LOINC代码）以创建跨多个机构的综合医学数据库的需求日益增长。然而，由于医学术语（如测试名称）的日语语料库有限，日语中的自然语言处理（NLP）比英语面临更大的挑战。为了解决这一限制，我们开发了一种基于机器学习的方法，该方法通过利用测试结果值将内部代码映射到LOINC代码，而不依赖于需要NLP的测试名称。使用这种方法，我们在本研究中针对的80.4%的分析物上实现了较高的映射准确率（70%或更高）。所提出的方法便于在NLP具有挑战性的语言中更轻松地映射到标准化代码，确保无论源数据语言如何都能准确映射到LOINC代码。
