Ochs Christopher, Case James T, Perl Yehoshua
Computer Science Department, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA.
National Library of Medicine/National Institutes of Health, Bethesda, MD 20894, USA.
J Biomed Inform. 2017 Mar;67:101-116. doi: 10.1016/j.jbi.2017.02.006. Epub 2017 Feb 12.
Thousands of changes are applied to SNOMED CT's concepts during each release cycle. These changes are the result of efforts to improve or expand the coverage of health domains in the terminology. Understanding which concepts changed, how they changed, and the overall impact of a set of changes is important for editors and end users. Each SNOMED CT release comes with delta files, which identify all of the individual additions and removals of concepts and relationships. These files typically contain tens of thousands of individual entries, overwhelming users. They also do not identify the editorial processes that were applied to individual concepts and they do not capture the overall impact of a set of changes on a subhierarchy of concepts. In this paper we introduce a methodology and accompanying software tool called a SNOMED CT Visual Semantic Delta ("semantic delta" for short) to enable a comprehensive review of changes in SNOMED CT. The semantic delta displays a graphical list of editing operations that provides semantics and context to the additions and removals in the delta files. However, there may still be thousands of editing operations applied to a set of concepts. To address this issue, a semantic delta includes a visual summary of changes that affected sets of structurally and semantically similar concepts. The software tool for creating semantic deltas offers views of various granularities, allowing a user to control how much change information they view. In this tool a user can select a set of structurally and semantically similar concepts and review the editing operations that affected their modeling. The semantic delta methodology is demonstrated on SNOMED CT's Bacterial infectious disease subhierarchy, which has undergone a significant remodeling effort over the last two years.
在每个发布周期中，SNOMED CT的概念会有数千处更改。这些更改是为了改进或扩大术语表中健康领域覆盖范围而做出努力的结果。了解哪些概念发生了变化、如何变化以及一组更改的总体影响，对编辑人员和最终用户来说都很重要。每个SNOMED CT版本都附带增量文件，这些文件标识了概念和关系的所有个别添加和删除。这些文件通常包含数以万计的个别条目，让用户应接不暇。它们也没有识别应用于个别概念的编辑过程，也没有捕捉一组更改对概念子层次结构的总体影响。在本文中，我们介绍了一种方法以及一个名为SNOMED CT视觉语义增量（简称为“语义增量”）的配套软件工具，以便全面审查SNOMED CT中的更改。语义增量显示了一个编辑操作的图形列表，为增量文件中的添加和删除提供语义和上下文。然而，应用于一组概念的编辑操作可能仍有数千个。为了解决这个问题，语义增量包括对影响结构和语义相似概念集的更改的可视化总结。用于创建语义增量的软件工具提供了各种粒度的视图，允许用户控制他们查看的更改信息的数量。在这个工具中，用户可以选择一组结构和语义相似的概念，并查看影响其建模的编辑操作。语义增量方法在SNOMED CT的细菌感染性疾病子层次结构上得到了验证，该子层次结构在过去两年中经历了重大的重塑工作。
