A Study of Semantic Proximity between Archetype Terms Based on SNOMED CT Relationships
A Study of Semantic Proximity between Archetype Terms Based on SNOMED CT Relationships
复制标题
基于SNOMED CT关系的原型术语之间的语义邻近性研究
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Tellado
中科院分区：
文献类型：
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作者：
J. L. Allones;David R. Penas;M. Taboada;D. Martínez;S. Tellado
OpenEHR原型已被建议作为详细描述电子医疗记录数据模型的标准，作为实现临床系统之间互操作性的一种手段。但是，将这些临床数据模型的术语映射到术语系统（如SNOMED CT）是提供所需互操作性的关键步骤。通过这项研究，我们的目的是更好地了解原型临床信息是如何使用SNOMED CT关系作为参考语义相关。为此，我们开发了一种自动化的方法，将原型术语绑定到SNOMED CT术语。我们的方法揭示了高度的语义相似性的条款建模的原型和层次和逻辑关系所涵盖的SNOMED CT。研究发现，超过75%的原型词与同一原型词之间存在语义关联。考虑到这一点，我们的方法适用于词汇和语言资源的术语关系为基础的技术相结合。一组25个临床原型与477约束条件被用来测试的方法。其中，378个术语（79%）与96%的准确率，76%的召回率相关联。我们的方法已被证明可以利用SNOMED CT关系结构，使总召回率提高10%。因此，这项工作表明，它是可以自动映射到一个标准的术语，具有较高的精度和召回率，与适当的上下文和语义信息的帮助下，这两个模型。
The OpenEHR archetypes have been suggested as a standard for detailing data models of electronic healthcare records, as a means of achieving interoperability between clinical systems. But, mapping terms of these clinical data models to a terminology system, such as SNOMED CT, is a crucial step to provide the required interoperability. Through this study, we aim to understand better how archetype clinical information is semantically related using SNOMED CT relationships as a reference. For this purpose, we developed an automated approach to bind archetype terms to the SNOMED CT terminology. Our method revealed a high degree of semantic similarity between the terms modeled in the archetypes and the hierarchical and logical relationships covered by SNOMED CT. It has been detected that more than 75% of the archetype terms are semantically related to other terms of the same archetype. Taking this into account, our approach applies a combination of terminological relationships-based techniques with lexical and linguistic resources. A set of 25 clinical archetypes with 477 bound terms was used to test the method. Of these, 378 terms (79%) were linked with 96% precision, 76% recall. Our approach has proven to take advantage of the SNOMED CT relationship structure, increasing the total recall by 10%. Therefore, this work shows that it is possible to automatically map archetype terms to a standard terminology with a high precision and recall, with the help of appropriate contextual and semantic information of both models.