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J Biomed Inform. 2018 Feb;78:177-184. doi: 10.1016/j.jbi.2017.12.010. Epub 2017 Dec 20.

Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs.

Author information

1
Department of Computer Science, University of Kentucky, Lexington, KY, USA; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA. Electronic address: licong.cui@uky.edu.
2
National Library of Medicine, Bethesda, MD, USA.
3
Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.
4
Department of Computer Science, University of Kentucky, Lexington, KY, USA; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA; Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.

Abstract

OBJECTIVE:

We introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations.

METHODS:

Our approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT's IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor concepts within the non-lattice subgraph. In stage 3, subset inclusion relations between the lexical attribute sets of each pair of concepts in each non-lattice subgraph are compared to existing IS-A relations in SNOMED CT. For concept pairs within each non-lattice subgraph, if a subset relation is identified but an IS-A relation is not present in SNOMED CT IS-A transitive closure, then a missing IS-A relation is reported. The September 2017 release of SNOMED CT (US edition) was used in this investigation.

RESULTS:

A total of 14,380 non-lattice subgraphs were extracted, from which we suggested a total of 41,357 missing IS-A relations. For evaluation purposes, 200 non-lattice subgraphs were randomly selected from 996 smaller subgraphs (of size 4, 5, or 6) within the "Clinical Finding" and "Procedure" sub-hierarchies. Two domain experts confirmed 185 (among 223) suggested missing IS-A relations, a precision of 82.96%.

CONCLUSIONS:

Our results demonstrate that analyzing the lexical features of concepts in non-lattice subgraphs is an effective approach for auditing SNOMED CT.

KEYWORDS:

Biomedical ontologies; Lexical attributes; Non-lattice subgraphs; Quality assurance; SNOMED CT

PMID:
29274386
PMCID:
PMC5835197
DOI:
10.1016/j.jbi.2017.12.010
[Indexed for MEDLINE]
Free PMC Article

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