Quality assurance in LOINC using Description Logic

AMIA Annu Symp Proc. 2012;2012:1099-108. Epub 2012 Nov 3.


Objective: To assess whether errors can be found in LOINC by changing its representation to OWL DL and comparing its classification to that of SNOMED CT.

Methods: We created Description Logic definitions for LOINC concepts in OWL and merged the ontology with SNOMED CT to enrich the relatively flat hierarchy of LOINC parts. LOINC - SNOMED CT mappings were acquired through UMLS. The resulting ontology was classified with the ConDOR reasoner.

Results: Transformation into DL helped to identify 427 sets of logically equivalent LOINC codes, 676 sets of logically equivalent LOINC parts, and 239 inconsistencies in LOINC multiaxial hierarchy. Automatic classification of LOINC and SNOMED CT combined increased the connectivity within LOINC hierarchy and increased its coverage by an additional 9,006 LOINC codes.

Conclusions: LOINC is a well-maintained terminology. While only a relatively small number of logical inconsistencies were found, we identified a number of areas where LOINC could benefit from the application of Description Logic.

Publication types

  • Research Support, N.I.H., Intramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Logical Observation Identifiers Names and Codes*
  • Programming Languages*
  • Quality Control
  • Systematized Nomenclature of Medicine
  • Unified Medical Language System