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MAP-CARE: Enhancing Cross-Lingual Medical Intervention Terms
Hugo Guillen-Ramirez1, Karen Triep2, Christophe Gaudet-Blavignac3
1Department of Visceral Surgery and Medicine, University Hospital, Bern, Switzerland.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces MAP-CARE, a framework using Large Language Models (LLMs) to integrate multilingual medical procedure data. It enhances cross-lingual retrieval and interoperability across diverse healthcare systems.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Healthcare Data Integration
Background:
- Current systems struggle with cross-lingual medical procedure data alignment.
- Non-English healthcare systems present significant data integration challenges.
Purpose of the Study:
- To develop a pipeline for cross-lingual retrieval and integration of medical procedures data.
- To enhance interoperability across languages and healthcare systems.
Main Methods:
- Developed MAP-CARE, a novel framework leveraging Large Language Models (LLMs).
- Utilized semantic embeddings for translating and transforming medical procedures into a unified multilingual space.
Main Results:
- MAP-CARE demonstrated high accuracy in translating and mapping clinical terms.
- Achieved up to 90% accuracy in cross-language translation of procedure codes across English, German, French, and Italian.
Conclusions:
- MAP-CARE provides a flexible, scalable, and robust solution.
- Enables multilingual and cross-system integration of medical procedural data.
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