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

Area of Science:

• Medical Informatics
• Ontology Engineering
• Knowledge Representation

Background:

• The Visualization of Concept in Medicine (VCM) uses icons for medical concepts.
• Integrating VCM with standard terminologies like SNOMED CT requires mapping icons to terms.
• Both VCM and SNOMED CT possess compositional semantics.

Purpose of the Study:

• To develop and assess a semi-automatic semantic method for mapping SNOMED CT concepts to VCM icons.
• To leverage the semantic formalization of both SNOMED CT (description logic) and VCM (OWL ontology).

Main Methods:

• A semi-automatic semantic mapping approach was employed.
• It involved manual mapping of foundational VCM ontology concepts.
• Subsequent automatic generation of remaining mappings for SNOMED CT clinical findings.
• Evaluation by three experts on 100 randomly selected mappings from the SNOMED CT CORE subset.

Main Results:

• The method demonstrated promising performance in mapping SNOMED CT concepts to VCM icons.
• Experts confirmed the correctness of 82 out of 100 evaluated SNOMED CT concept to VCM icon links.
• The majority of identified errors were minor and easily correctable.

Conclusions:

• The proposed semi-automatic method is effective for linking SNOMED CT concepts with VCM icons.
• This approach facilitates the integration of iconic medical representations with standardized terminologies.
• Further refinement can improve the accuracy and efficiency of the mapping process.