PUBLICATIONS

Abstract

Auditing complex concepts in overlapping subsets of SNOMED.


Wang Y, Wei D, Xu J, Elhanan G, Perl Y, Halper M, Chen Y, Spackman KA, Hripcsak G

AMIA Annu Symp Proc. November 2008:273-7.

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.


Wang Y, Wei D, Xu J, Elhanan G, Perl Y, Halper M, Chen Y, Spackman KA, Hripcsak G Auditing complex concepts in overlapping subsets of SNOMED. 
AMIA Annu Symp Proc. November 2008:273-7.

PMID | PMCID