Auditing complex concepts in overlapping subsets of SNOMED.
Document Type
Article
Publication Date
1-1-2008
Abstract
Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries.
Identifier
67349192600 (Scopus)
Publication Title
AMIA Annual Symposium Proceedings AMIA Symposium AMIA Symposium
e-ISSN
1942597X
PubMed ID
18998838
First Page
273
Last Page
277
Grant
R01LM008912
Fund Ref
U.S. National Library of Medicine
Recommended Citation
Wang, Yue; Wei, Duo; Xu, Junchuan; Elhanan, Gai; Perl, Yehoshua; Halper, Michael; Chen, Yan; Spackman, Kent A.; and Hripcsak, George, "Auditing complex concepts in overlapping subsets of SNOMED." (2008). Faculty Publications. 12933.
https://digitalcommons.njit.edu/fac_pubs/12933
