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VCM (Visualization of Concept in Medicine) is an iconic language forrepresenting key medical concepts by icons. However, the use of this languagewith reference terminologies, such as SNOMED CT, will require the mapping ofits icons to the terms of these terminologies. Here, we present and evaluate asemi-automatic semantic method for the mapping of SNOMED CT concepts to VCMicons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT isexpressed in description logic and VCM semantics are formalized in an OWLontology. The proposed method involves the manual mapping of a limited numberof underlying concepts from the VCM ontology, followed by automatic generationof the rest of the mapping. We applied this method to the clinical findings ofthe SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated bythree experts. The results obtained were promising, with 82 of the SNOMED CTconcepts correctly linked to VCM icons according to the experts. Most of theerrors were easy to fix.

A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons