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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 372-391 |
| Number of pages | 20 |
| Journal | International Journal of Data Mining and Bioinformatics |
| Volume | 15 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2016 |
All Science Journal Classification (ASJC) codes
- Information Systems
- General Biochemistry, Genetics and Molecular Biology
- Library and Information Sciences
Keywords
- Contextual auditing
- Lexical similarities
- Modelling inconsistencies
- SNOMED CT
- Structural dissimilarities
- Structural indicators
- Terminology auditing
- Terminology quality assurance