ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
AI Mapping of In-House Codes to LOINC Codes Using Laboratory Test Results Excluding Test Names: Toward International Sharing of Medical Data.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
Identifiers
40502252PMC12150744What OpenQuestion holds
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