title: Scalability of abstraction-network-based quality assurance to large SNOMED hierarchies.
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.
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: Christopher
authors.initials: C
authors.lastname: Ochs
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: Yehoshua
authors.initials: Y
authors.lastname: Perl
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: James
authors.initials: J
authors.lastname: Geller
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: Michael
authors.initials: M
authors.lastname: Halper
authors.affiliation: New York Institute of Technology, New York, NY;
authors.forename: Huanying
authors.initials: H
authors.lastname: Gu
authors.affiliation: BMCC, CUNY, New York, NY.
authors.forename: Yan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: New Jersey Institute of Technology, Newark, NJ;
authors.forename: Gai
authors.initials: G
authors.lastname: Elhanan
mesh_terms: D001185:Artificial Intelligence; D008722:Methods; D011786:Quality Control; D039061:Systematized Nomenclature of Medicine; D009626:Terminology as Topic