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DOI: 10.1055/s-0038-1634079
A Framework for Characterizing Terminological Systems
Publication History
Publication Date:
06 February 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035037/200603/lookinside/thumbnails/10-1055-s-0038-1634079-1.jpg)
Summary
Objectives: The notion of a terminological system (TS) is complex due to the broad range of systems, applications, and clinical domains. A uniform approach to describe the characteristics of TSs is lacking. This impedes furthering understanding, applicability, mutual comparison and development of TSs. For these reasons we propose a terminological systems characterization framework.
Methods: Relevant issues pertaining to TSs and terminology servers have been extracted from literature describing requirements and functionality of TSs. From these issues, features have been distilled and further refined. A categorization has been developed to provide a convenient arrangement of these features.
Results: The framework distinguishes between application-dependent and application-independent features of TSs. Definitions are provided for measures of content coverage, which was identified as the only application-dependent feature. Application-independent features are categorized along two axes: their respective type of TS and the particular element within that system, i.e. the formalism, the content, or the functionality. For each feature we provide an explicit question, the answer to which yields a feature value. The framework has been applied to SNOMED CT and the CLUE browser.
Conclusions: We present and apply a framework to support a feature-based characterization of terminological systems. Standardized methods for content coverage studies reduce the effort of assessing the applicability of a TS for a specific clinical setting. A two-axial categorization provides a convenient arrangement of the large number of application-independent features. Application of the framework increases comparability of terminological systems. This framework may also help TS developers determine how their system can be improved.
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