Definitions and Qualifiers in SNOMED CT

Summary

Objectives: An important feature of SNOMED CT is post-coordination, which is enabled by the SNOMED CT representation specifying whether a relationship is a defining or a qualifier relationship. In this paper the use of qualifier relationships in SNOMED CT is analyzed, as well as the extent to which qualifiers interact with defining relationships, so that pre-coordinated concepts can also be post-coordinated.

Methods: The July 2007 release of SNOMED CT was imported into a database. Analyses were performed by querying this database.

Results: Qualifier relationships occur in 10 out of 61 types of attribute relationships, and it is shown that generally pre-coordinated concepts cannot be constructed by applying post-coordination using qualifier relationships. Most of the qualifier relationships have generic target concepts, making it possible to construct concepts which are not clinically sensible. A logic-based representation is proposed to overcome the drawbacks of the current model.

Conclusions: Defining and qualifier relationships both enable post-coordination in SNOMED CT. Introducing qualifiers for more types of relationships, and using qualifier relationships with more specific target concepts will further improve post-coordination in SNOMED CT.

Keywords

SNOMED CT - nomenclature - relationships

• References

• 1 Rector AL, Rogers J, Taweel A. Models and inference methods for clinical systems: a principled approach. Stud Health Technol Inform 2004; 107 Pt 1 79-83.
• 2 Rector AL. The interface between information, terminology, and inference models. Stud Health Technol Inform 2001; 84 Pt 1 246-250.
• 3 Spackman KA. et al. Role grouping as an extension to the description logic of Ontylog, motivated by concept modeling in SNOMED. Proc AMIA Symp 2000 pp 712-716.
• 4 Minsky M. A framework for representing knowledge. In: Winston P. editor. The Psychology of Computer Vision. New York: McGraw-Hill; 1975. pp 211-277.
• 5 Rector AL. et al. The GRAIL Concept Modelling Language for Medical Terminology. Artificial Intelligence in Medicine 1997; 9: 139-171.
• 6 Bechhofer S, Goble C. Using Description Logics to Drive Query Interfaces. In: 1997 International Workshop on Description Logics (DL97). Gif sur Yvette (Paris), France: 1997
• 7 Baader F. et al. In:. Baaader F. et al. (eds). The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge: University Press; 2003. p 555.
• 8 de Coronado S. et al. NCI Thesaurus: using science-based terminology to integrate cancer research results. In: Proceedings from Medinfo 2004. San Francisco, CA, USA: IOS Press; Amsterdam, The Netherlands 2004
• 9 Cornet R, Klein MCA. Representing and Using Template-Knowledge for a Medical Ontology in Protégé. In: Protégé Conference. Madrid: SMI; 2005
• 10 Schulz S. et al. SNOMED reaching its adolescence: Ontologists’ and logicians’ health check. Int J Med Inform 2008
• 11 Hartel FW. et al. Modeling a description logic vocabulary for cancer research. Journal of Biomedical Informatics 2005; 38 (02) 114-129.
• 12 Cornet R, Abu-Hanna A. Description logic-based methods for auditing frame-based medical terminological systems. Artificial Intelligence in Medicine 2005; 34 (03) 201-217.
• 13 Seidenberg J, Rector AL. Techniques for Segmenting Large Description Logic Ontologies. In: Workshop on Ontology Management: Searching, Selection, Ranking, and Segmentation. 3rd Int Conf Knowledge Capture (K-Cap). Banff, Canada: 2005
• 14 Rector AL, Brandt S. Why do it the hard way?. The Case for an Expressive Description Logic for SNOMED. J Am Med Inform Assoc; 2008