An Agentic Model Context Protocol Framework for Medical Concept Standardization
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909770281123840 |
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| author | Ahn, Jaerong Wen, Andrew Wang, Nan Jia, Heling Yue, Zhiyi Fu, Sunyang Liu, Hongfang |
| author_facet | Ahn, Jaerong Wen, Andrew Wang, Nan Jia, Heling Yue, Zhiyi Fu, Sunyang Liu, Hongfang |
| contents | The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) provides a standardized representation of heterogeneous health data to support large-scale, multi-institutional research. One critical step in data standardization using OMOP CDM is the mapping of source medical terms to OMOP standard concepts, a procedure that is resource-intensive and error-prone. While large language models (LLMs) have the potential to facilitate this process, their tendency toward hallucination makes them unsuitable for clinical deployment without training and expert validation. Here, we developed a zero-training, hallucination-preventive mapping system based on the Model Context Protocol (MCP), a standardized and secure framework allowing LLMs to interact with external resources and tools. The system enables explainable mapping and significantly improves efficiency and accuracy with minimal effort. It provides real-time vocabulary lookups and structured reasoning outputs suitable for immediate use in both exploratory and production environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03828 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Agentic Model Context Protocol Framework for Medical Concept Standardization Ahn, Jaerong Wen, Andrew Wang, Nan Jia, Heling Yue, Zhiyi Fu, Sunyang Liu, Hongfang Artificial Intelligence The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) provides a standardized representation of heterogeneous health data to support large-scale, multi-institutional research. One critical step in data standardization using OMOP CDM is the mapping of source medical terms to OMOP standard concepts, a procedure that is resource-intensive and error-prone. While large language models (LLMs) have the potential to facilitate this process, their tendency toward hallucination makes them unsuitable for clinical deployment without training and expert validation. Here, we developed a zero-training, hallucination-preventive mapping system based on the Model Context Protocol (MCP), a standardized and secure framework allowing LLMs to interact with external resources and tools. The system enables explainable mapping and significantly improves efficiency and accuracy with minimal effort. It provides real-time vocabulary lookups and structured reasoning outputs suitable for immediate use in both exploratory and production environments. |
| title | An Agentic Model Context Protocol Framework for Medical Concept Standardization |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03828 |