Scalability of abstraction-network-based quality assurance to large SNOMED hierarchies.

Christopher Ochs, Yehoshua Perl, James Geller, Michael Halper, Huanying Gu, Yan Chen, Gai Elhanan

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Abstraction networks are compact summarizations of terminologies used to support orientation and terminology quality assurance (TQA). Area taxonomies and partial-area taxonomies are abstraction networks that have been successfully employed in support of TQA of small SNOMED CT hierarchies. However, nearly half of SNOMED CT's concepts are in the large Procedure and Clinical Finding hierarchies. Abstraction network derivation methodologies applied to those hierarchies resulted in taxonomies that were too large to effectively support TQA. A methodology for deriving sub-taxonomies from large taxonomies is presented, and the resultant smaller abstraction networks are shown to facilitate TQA, allowing for the scaling of our taxonomy-based TQA regimen to large hierarchies. Specifically, sub-taxonomies are derived for the Procedure hierarchy and a review for errors and inconsistencies is performed. Concepts are divided into groups within the sub-taxonomy framework, and it is shown that small groups are statistically more likely to harbor erroneous and inconsistent concepts than large groups.

Original languageEnglish (US)
Pages (from-to)1071-1080
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2013
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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