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Using description logics to evaluate the consistency of drug-class membership relations in NDF-RT

Overview of attention for article published in Journal of Biomedical Semantics, March 2015
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Title
Using description logics to evaluate the consistency of drug-class membership relations in NDF-RT
Published in
Journal of Biomedical Semantics, March 2015
DOI 10.1186/s13326-015-0007-3
Pubmed ID
Authors

Rainer Winnenburg, Jonathan M Mortensen, Olivier Bodenreider

Abstract

The NDF-RT (National Drug File Reference Terminology) is an ontology, which describes drugs and their properties and supports computerized physician order entry systems. NDF-RT's classes are mostly specified using only necessary conditions and lack sufficient conditions, making its use limited until recently, when asserted drug-class relations were added. The addition of these asserted drug-class relations presents an opportunity to compare them with drug-class relations that can be inferred using the properties of drugs and drug classes in NDF-RT. We enriched NDF-RT's drug-classes with sufficient conditions, added property equivalences, and then used an OWL reasoner to infer drug-class membership relations. We compared the inferred class relations to the recently added asserted relations derived from FDA Structured Product Labels. The inferred and asserted relations only match in about 50% of the cases, due to incompleteness of the drug descriptions and quality issues in the class definitions. This investigation quantifies and categorizes the disparities between asserted and inferred drug-class relations and illustrates issues with class definitions and drug descriptions. In addition, it serves as an example of the benefits DL can add to ontology development and evaluation.

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Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 40%
Student > Ph. D. Student 2 20%
Librarian 1 10%
Student > Doctoral Student 1 10%
Unknown 2 20%
Readers by discipline Count As %
Computer Science 5 50%
Medicine and Dentistry 2 20%
Agricultural and Biological Sciences 1 10%
Unknown 2 20%