Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies
Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies
复制标题
DOI：
10.1136/amiajnl-2014-003151
复制
发表时间：
2015-05-01
影响因子：
6.4
通讯作者：
Wei, Zhi
中科院分区：
文献类型：
--
作者：
Ochs, Christopher;Geller, James;Wei, Zhi
标准术语可能庞大而复杂，使其质量保证具有挑战性。一些术语质量保证（TQA）方法是基于抽象网络（AbN），紧凑的术语摘要。我们已经测试了AbN和相关的TQA方法在小术语层次结构上的性能。然而，某些标准术语（例如SNOMED）由非常大的层次结构组成。将AbN TQA技术扩展到这样的层次结构构成了重大挑战。我们提出了一个可扩展的基于主题的方法为ABN TQA.Methods一个创新的技术，提出了一种新的基于主题的ABN称为大层次结构的子分类的缩放TQA。新的假设浓度的错误概念内的AbN的介绍，以指导可扩展的TQA.Results我们测试的TQA方法为基础的亚分类出血亚层次结构中的SNOMED的大型临床发现层次结构。为了验证错误集中假设，三位领域专家审查了300个概念的样本。一项基于共识的评价确定了87个错误概念。的子分类为基础的TQA方法被证明发现统计上显着更多的错误概念相比，一个控制sample.Discussion TQA方法的可扩展性是一个挑战，大型标准系统，如SNOMED。我们展示了创新的基于主题的TQA技术，通过识别在子分类中具有较高错误可能性的概念组。通过按主题审查大型层次结构来实现可扩展性。结论用于扩展AbN推导的创新方法和TQA方法已被证明在SNOMED的最大层次结构中成功执行。
Objective Standards terminologies may be large and complex, making their quality assurance challenging. Some terminology quality assurance (TQA) methodologies are based on abstraction networks (AbNs), compact terminology summaries. We have tested AbNs and the performance of related TQA methodologies on small terminology hierarchies. However, some standards terminologies, for example, SNOMED, are composed of very large hierarchies. Scaling AbN TQA techniques to such hierarchies poses a significant challenge. We present a scalable subject-based approach for AbN TQA.Methods An innovative technique is presented for scaling TQA by creating a new kind of subject-based AbN called a subtaxonomy for large hierarchies. New hypotheses about concentrations of erroneous concepts within the AbN are introduced to guide scalable TQA.Results We test the TQA methodology for a subject-based subtaxonomy for the Bleeding subhierarchy in SNOMED's large Clinical finding hierarchy. To test the error concentration hypotheses, three domain experts reviewed a sample of 300 concepts. A consensus-based evaluation identified 87 erroneous concepts. The subtaxonomy-based TQA methodology was shown to uncover statistically significantly more erroneous concepts when compared to a control sample.Discussion The scalability of TQA methodologies is a challenge for large standards systems like SNOMED. We demonstrated innovative subject-based TQA techniques by identifying groups of concepts with a higher likelihood of having errors within the subtaxonomy. Scalability is achieved by reviewing a large hierarchy by subject.Conclusions An innovative methodology for scaling the derivation of AbNs and a TQA methodology was shown to perform successfully for the largest hierarchy of SNOMED.