Geller James, Ochs Christopher, Perl Yehoshua, Xu Junchuan
New Jersey Institute of Technology, Newark, NJ, USA.
AMIA Annu Symp Proc. 2012;2012:237-46. Epub 2012 Nov 3.
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.
医学术语庞大且复杂。错误常常隐藏在这种复杂性之中。我们的目标是找出此类错误，从大型术语表中派生抽象网络有助于实现这一目标。抽象网络保留了重要特征，但消除了许多细微细节，而这些细节通常对识别错误并无帮助。为这类抽象网络提供可视化效果，可使审核人员快速聚焦于术语表中感兴趣的元素，从而帮助他们进行审核。此前我们为SNOMED CT引入了区域分类法和部分区域分类法。在本文中，我们定义了两种先进的新型抽象网络，即关系约束部分区域子分类法和根约束部分区域子分类法，并展示了它们的优势。我们还介绍了BLUSNO，这是一款用于快速生成和可视化这些SNOMED CT抽象网络的创新软件工具。BLUSNO是一个动态交互式系统，可快速访问有关SNOMED CT的组织良好的信息。
