A contextual auditing method for SNOMED CT concepts

Document Type

Article

Publication Date

1-1-2016

Abstract

SNOMED CT has been regarded as the most prominent clinical health terminology to be used in Electronic Health Records. However, modelling inconsistencies are preventing SNOMED CT from providing proper support for clinical use. This study introduces positional similarity sets as an effective contextual technique to identify such inconsistencies and improve the modelling of SNOMED CT concepts. Positional similarity sets are sets of lexically similar concepts having only one different word at the same position of their names. A technique to incorporate three structural indicators into the selected sets is provided to improve the likelihood of finding inconsistently modelled concepts. The results show that the likelihood of finding inconsistencies using such positional similarity sets is up to 41.6%. Such quality assurance methods can be used to supplement IHTSDO's own efforts in order to improve the quality of SNOMED CT.

Identifier

84981499483 (Scopus)

Publication Title

International Journal of Data Mining and Bioinformatics

External Full Text Location

https://doi.org/10.1504/IJDMB.2016.078153

e-ISSN

17485681

ISSN

17485673

First Page

372

Last Page

391

Issue

4

Volume

15

This document is currently not available here.

Share

COinS