Taxonomies Supporting Orientation, Navigation and Auditing of Terminologies

New Jersey Institute Of Technology, Newark NJ

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Abstract

[unreadable] DESCRIPTION (provided by applicant): [unreadable] [unreadable] We have seen the emergence of a generation of medical terminologies satisfying[unreadable] systematic inheritance of relationships. Examples include SNOMED CT (the[unreadable] Systematized Nomenclature of Medicine - Clinical Terms), the National Cancer Institute[unreadable] Thesaurus (NCIT), Kaiser's Convergent Medical Terminology (CMT), the VA's internal[unreadable] enterprise terminology, the Foundational Model of Anatomy (FMA), and the Medical[unreadable] Entities Dictionary (MED). These terminologies are of substantial size and complexity.[unreadable] Due to this, user orientation is difficult, especially given the fact that more knowledge is[unreadable] continually being added to them. Orientation and navigation capabilities are essential for[unreadable] effective terminology maintenance and usage (for example, in decision-support systems,[unreadable] patient records, and healthcare administrative systems). One cannot reasonably be[unreadable] expected to maintain or use a terminology reliably without them. We propose to design[unreadable] structural abstraction methodologies to derive novel terminological views called[unreadable] "taxonomies." These will form the bases for new techniques to support user orientation[unreadable] to and navigation of terminologies. The taxonomies will further aid in efficient auditing.[unreadable] A number of different levels of taxonomy will be developed. Our methodologies will[unreadable] utilize the IS-A relationship hierarchies and accompanying systematic relationship[unreadable] inheritance of this generation of terminologies. They will be based on new partitioning[unreadable] techniques, which will break down large collections of concepts into smaller units of[unreadable] structurally and semantically similar concepts that can be more easily handled and[unreadable] comprehended. One partitioning technique will be based on similar relationship structure,[unreadable] leading to the derivation of an abstraction network call the "area taxonomy." Another[unreadable] will further utilize semantic similarity based on common ancestry in the terminology's ISA[unreadable] hierarchy leading to the finer-grained "p-area taxonomy." Additional partitioning[unreadable] techniques---leading to further refined taxonomies---will focus on other structural[unreadable] features of terminologies, such as obtainment-pattern regions. Our abstraction[unreadable] methodologies will be general and applicable to a wide range of terminologies satisfying[unreadable] systematic inheritance. We will use SNOMED and the NCIT's genomics hierarchies as[unreadable] test-beds. We will demonstrate the utility of our taxonomy-based methodologies by[unreadable] defining various complexity measures with respect to the underlying terminology[unreadable] networks and by tracking terminology evolution.