Cross-domain Targeted Ontology Subsets for Annotation

Display Omitted We propose a computationally-efficient approach to ontology subset extraction.We use the SNOMED CORE subset as source; NDF-RT, RxNorm, and the UMLS Metathesaurus.We extract a targeted subset of drugs, RxNorm/CORE, 4% the size of RxNorm.Recall was better when annotating scientific abstracts than clinical texts.Ontology repositories with mappings look promising to extract subsets for annotation. 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.