Auditing complex concepts in overlapping subsets of SNOMED

AMIA Annu Symp Proc. 2008 Nov 6;2008:273-7.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Clinical Audit / methods*
  • Medical Errors / prevention & control*
  • Natural Language Processing*
  • Pattern Recognition, Automated / methods
  • Systematized Nomenclature of Medicine*
  • Terminology as Topic*
  • United States