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<p>Cross-lingual information retrieval limits global exchange of data because of the high diversity in the methods to classify, document and encode medical procedures. Traditional keyword-based or single-language systems are not able to align data from surgical and interventional procedures, especially from non-English healthcare systems. This study aims to develop a pipeline for cross-lingual retrieval and integration of medical procedures data. MAP-CARE is a novel framework that leverages Large Language Models (LLMs) for translating and transforming medical procedures into a unified multilingual embedding space. <b>S</b>emantic embeddings are used to enhance retrieval accuracy and interoperability across languages and healthcare systems. MAP-CARE demonstrated high accuracy in the translation and mapping of clinical terms. Its cross-language translation performance proved robust, achieving up to Acc@5 = 0.90 in translating procedure classification codes across English, German, French, and Italian. The cross-classification mapping workflow also showed high accuracy in aligning two different national procedure classifications, with exact and near matches exceeding 53.8% at the most granular level. MAP-CARE offers a flexible, scalable, and robust solution for the multilingual and cross-system integration of medical procedural data. Its innovative use of large language models (LLMs) combined with semantic embeddings sets a new standard for the accessibility and utility of multilingual medical information. The framework is designed for easy extension from a terminology file in CSV format and is publicly available.</p>

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LLM-augmented semantic embeddings enable Cross-Lingual mapping of medical procedure terms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月9日

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  • Hugo Guillen-Ramirez,
  • Karen Triep,
  • Christophe Gaudet-Blavignac,
  • Baljit Phull,
  • Guido Beldi,
  • Olga Endrich

摘要

Cross-lingual information retrieval limits global exchange of data because of the high diversity in the methods to classify, document and encode medical procedures. Traditional keyword-based or single-language systems are not able to align data from surgical and interventional procedures, especially from non-English healthcare systems. This study aims to develop a pipeline for cross-lingual retrieval and integration of medical procedures data. MAP-CARE is a novel framework that leverages Large Language Models (LLMs) for translating and transforming medical procedures into a unified multilingual embedding space. Semantic embeddings are used to enhance retrieval accuracy and interoperability across languages and healthcare systems. MAP-CARE demonstrated high accuracy in the translation and mapping of clinical terms. Its cross-language translation performance proved robust, achieving up to Acc@5 = 0.90 in translating procedure classification codes across English, German, French, and Italian. The cross-classification mapping workflow also showed high accuracy in aligning two different national procedure classifications, with exact and near matches exceeding 53.8% at the most granular level. MAP-CARE offers a flexible, scalable, and robust solution for the multilingual and cross-system integration of medical procedural data. Its innovative use of large language models (LLMs) combined with semantic embeddings sets a new standard for the accessibility and utility of multilingual medical information. The framework is designed for easy extension from a terminology file in CSV format and is publicly available.