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

TL;DR

This paper introduces a semi-automatic semantic approach to map SNOMED CT concepts to VCM icons, combining manual and automatic steps, and demonstrates promising accuracy in clinical concept mapping.

Contribution

It presents a novel semi-automatic method leveraging description logic and OWL ontologies for mapping SNOMED CT to VCM icons, reducing manual effort.

Findings

82% correct mapping accuracy

Most errors were easy to fix

Promising results for clinical concept mapping

Abstract

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