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Auditing complex concepts in overlapping subsets of SNOMED.

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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.