Auditing the Semantic Completeness of SNOMED CT Using Formal Concept Analysis
Auditing the Semantic Completeness of SNOMED CT Using Formal Concept Analysis
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DOI：
10.1197/jamia.m2541
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发表时间：
2009-01-01
影响因子：
6.4
通讯作者：
Chute, Christopher G.
中科院分区：
文献类型：
--
作者：
Jiang, Guoqian;Chute, Christopher G.
目的：本研究旨在开发和评估一种方法，用于审计的语义完整性的SNOMED CT内容使用形式概念分析（FCA）为基础的model.Design：我们开发了一个模型，用于形式化的正常形式的SNOMED CT表达式使用FCA。通过分析识别的匿名节点从模型中检索以进行评估。开发了两个准泊松回归模型，以测试匿名节点是否可以评估SNOMED CT内容的语义完整性（模型1），并测试这种完整性是否在2个临床领域之间存在差异（模型2）。数据从2个最大领域（手术和临床发现）中可能形成的所有背景中随机抽样。案例研究（n = 4）进行了随机选择的匿名节点样本validation.Measurements：在模型1中，结果变量是在一个上下文中完全定义的概念的数量，而解释变量是网格节点的数量和匿名节点的数量。在模型2中，结果变量是匿名节点的数量，解释变量是网格节点的数量和域的二元类别（程序/临床发现）。我们的研究结果显示，匿名节点的数量与上下文中完全定义的概念的数量呈显著负相关（p < 0.001）。此外，临床发现域的匿名节点比程序域少（p < 0.001）。案例研究表明，匿名节点是一个有效的索引审计SNOMED CT。结论：从FCA为基础的分析检索到的匿名节点是一个候选代理的语义完整性的SNOMED CT内容。我们的新的FCA为基础的方法可以是有用的审计的语义完整性SNOMED CT内容，或任何大型本体，内或跨域。
Objective: This study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)-based model.Design: We developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation.Measurements: In Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding).Results: A total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001.). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT.Conclusion: The anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.