Achieving Semantic Interoperability between Physiology Models and Clinical Data

B. Bono, S. Sammut, P. Grenon
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引用次数: 3

Abstract

The practice and research of biomedicine generates considerable quantities of data and model resources (DMRs). The RICORDO effort works closely with modelling communities in the physiology and pharmacology domains to provide a semantic interoperability framework that addresses obstacles to biomedical DMR sharing. The RICORDO framework adopts a core set of community supported standard reference ontologies with which to effect, and reason over, modelling resource metadata. In some cases, knowledge in reference ontologies that is critical to particular interoperability objectives may be incomplete. The specific objective discussed in this paper focuses on the derivation of semantic interoperability between cardiovascular physiology models and related clinical data. In particular, the aim of this work is to semantically infer the anatomical relationship between variables in the Guyton circulatory model and data annotated with vascular disease terms from SNOMED-CT and the International Classification of Disease (ICD-10). The cardiovascular knowledgebase in the Foundational Model of Anatomy (FMA) was curated to provide a more extensive coverage of terms and relations referred to in Guyton model variables and related clinical data. A knowledge representation of cardiovascular connectivity was also developed with which to infer the topological features of the cardiovascular system exported from the curated knowledgebase. This approach allowed the calculation of semantic distance between physiology model variables and disease terms on the basis of their involvement with specific cardiovascular structures. The resulting methodology and associated extended knowledgebase allow the comparison of DMR metadata arising from annotations that conform to the RICORDO ontology standard. In particular, this approach quantifiably and semantically relates physiology and disease concepts annotating mathematical models and clinical data.
实现生理模型和临床数据之间的语义互操作性
生物医学的实践和研究产生了大量的数据和模型资源(DMRs)。RICORDO努力与生理学和药理学领域的建模社区密切合作,提供语义互操作性框架,解决生物医学DMR共享的障碍。RICORDO框架采用了一组核心的社区支持的标准参考本体,通过这些本体可以对资源元数据进行建模和推理。在某些情况下,对特定互操作性目标至关重要的参考本体中的知识可能是不完整的。本文讨论的具体目标集中在心血管生理学模型和相关临床数据之间的语义互操作性的推导。特别是,这项工作的目的是从语义上推断Guyton循环模型中变量与SNOMED-CT和国际疾病分类(ICD-10)中血管疾病术语注释的数据之间的解剖关系。基础解剖学模型(FMA)中的心血管知识库旨在提供更广泛的盖顿模型变量和相关临床数据中涉及的术语和关系。心血管连通性的知识表示也被开发出来,用来推断心血管系统的拓扑特征。这种方法允许计算生理模型变量和疾病术语之间的语义距离,基于它们与特定心血管结构的关系。由此产生的方法和相关的扩展知识库允许比较由符合RICORDO本体标准的注释产生的DMR元数据。特别是,这种方法定量地和语义地关联生理学和疾病概念,注释数学模型和临床数据。
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