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Method for mapping the French CCAM terminology to the UMLS metathesaurus
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The French coding system of surgical procedures, the Classification Commune des Actes Médicaux (CCAM), is used in France for DRG databases and fee for services payment. Mapping between CCAM and other clinical procedures terminologies by the means of UMLS metathesaurus is essential in order to increase semantic interoperability between different healthcare terminologies and between different case mix systems. In a previous work the CISMeF team used an automatic approach to map CCAM descriptors to the French part of the UMLS metathesaurus. In another way for the French funded research project InterSTIS, we performed a mapping using MetaMap based on the top level semantic structure descriptors of anatomy and action of CCAM translated from French to English. This paper aims to present this new approach and to compare the results with the previous one. The combination of both approaches significantly improved the coverage of the mapping to 68 % for both descriptors and 95 % for at least one descriptor.
Title: Method for mapping the French CCAM terminology to the UMLS metathesaurus
Description:
The French coding system of surgical procedures, the Classification Commune des Actes Médicaux (CCAM), is used in France for DRG databases and fee for services payment.
Mapping between CCAM and other clinical procedures terminologies by the means of UMLS metathesaurus is essential in order to increase semantic interoperability between different healthcare terminologies and between different case mix systems.
In a previous work the CISMeF team used an automatic approach to map CCAM descriptors to the French part of the UMLS metathesaurus.
In another way for the French funded research project InterSTIS, we performed a mapping using MetaMap based on the top level semantic structure descriptors of anatomy and action of CCAM translated from French to English.
This paper aims to present this new approach and to compare the results with the previous one.
The combination of both approaches significantly improved the coverage of the mapping to 68 % for both descriptors and 95 % for at least one descriptor.
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