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J Biomed Inform. 2014 Feb;47:105-11. doi: 10.1016/j.jbi.2013.09.011. Epub 2013 Oct 1.

Cross-domain targeted ontology subsets for annotation: the case of SNOMED CORE and RxNorm.

Author information

1
Stanford Center for Biomedical Informatics Research, Stanford University, Medical School Office Building, Room X-215, 1265 Welch Road, Stanford, CA 94305-5479, USA; Department of Computer Languages and Systems, University of the Basque Country UPV/EHU, Manuel de Lardizabal 1, 20018 Donostia-San Sebastián, Spain. Electronic address: plopezgarcia@gmail.com.
2
Stanford Center for Biomedical Informatics Research, Stanford University, Medical School Office Building, Room X-215, 1265 Welch Road, Stanford, CA 94305-5479, USA.
3
Department of Computer Languages and Systems, University of the Basque Country UPV/EHU, Manuel de Lardizabal 1, 20018 Donostia-San Sebastián, Spain.

Abstract

The benefits of using ontology subsets versus full ontologies are well-documented for many applications. In this study, we propose an efficient subset extraction approach for a domain using a biomedical ontology repository with mappings, a cross-ontology, and a source subset from a related domain. As a case study, we extracted a subset of drugs from RxNorm using the UMLS Metathesaurus, the NDF-RT cross-ontology, and the CORE problem list subset of SNOMED CT. The extracted subset, which we termed RxNorm/CORE, was 4% the size of the full RxNorm (0.4% when considering ingredients only). For evaluation, we used CORE and RxNorm/CORE as thesauri for the annotation of clinical documents and compared their performance to that of their respective full ontologies (i.e., SNOMED CT and RxNorm). The wide range in recall of both CORE (29-69%) and RxNorm/CORE (21-35%) suggests that more quantitative research is needed to assess the benefits of using ontology subsets as thesauri in annotation applications. Our approach to subset extraction, however, opens a door to help create other types of clinically useful domain specific subsets and acts as an alternative in scenarios where well-established subset extraction techniques might suffer from difficulties or cannot be applied.

KEYWORDS:

Annotation; NDF-RT; Ontologies; RxNorm; SNOMED CT; UMLS

PMID:
24095962
PMCID:
PMC3951555
DOI:
10.1016/j.jbi.2013.09.011
[Indexed for MEDLINE]
Free PMC Article

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