Auditing complex concepts in overlapping subsets of SNOMED.

Yue Wang, Duo Wei, Junchuan Xu, Gai Elhanan, Yehoshua Perl, Michael Halper, Yan Chen, Kent A. Spackman, George Hripcsak

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)273-277
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2008

All Science Journal Classification (ASJC) codes

  • General Medicine


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