title: New abstraction networks and a new visualization tool in support of auditing the SNOMED CT content.
abstract: Medical terminologies are large and complex. Frequently, errors are hidden in this complexity. Our objective is to find such errors, which can be aided by deriving abstraction networks from a large terminology. Abstraction networks preserve important features but eliminate many minor details, which are often not useful for identifying errors. Providing visualizations for such abstraction networks aids auditors by allowing them to quickly focus on elements of interest within a terminology. Previously we introduced area taxonomies and partial area taxonomies for SNOMED CT. In this paper, two advanced, novel kinds of abstraction networks, the relationship-constrained partial area subtaxonomy and the root-constrained partial area subtaxonomy are defined and their benefits are demonstrated. We also describe BLUSNO, an innovative software tool for quickly generating and visualizing these SNOMED CT abstraction networks. BLUSNO is a dynamic, interactive system that provides quick access to well organized information about SNOMED CT.
authors.affiliation: New Jersey Institute of Technology, Newark, NJ, USA.
authors.forename: James
authors.initials: J
authors.lastname: Geller
authors.forename: Christopher
authors.initials: C
authors.lastname: Ochs
authors.forename: Yehoshua
authors.initials: Y
authors.lastname: Perl
authors.forename: Junchuan
authors.initials: J
authors.lastname: Xu
mesh_terms: D001185:Artificial Intelligence; D002965:Classification; D011786:Quality Control; D039061:Systematized Nomenclature of Medicine