临床和临床前术语之间的映射:eTRANSAFE的罗塞塔石碑方法。

IF 2 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Erik M van Mulligen, Rowan Parry, Johan van der Lei, Jan A Kors
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引用次数: 0

摘要

背景:eTRANSAFE项目开发了支持转化研究的工具。该项目的挑战之一是将临床前和临床数据结合起来,这些数据用不同的术语和粒度编码,并以单一的预先协调的临床概念和来自不同术语的临床前概念的组合表示。本研究开发并评估了Rosetta Stone方法,该方法将临床前概念的组合映射到临床,预先协调的概念,允许不同程度的映射准确性。方法:eTRANSAFE中使用的临床前和临床术语的概念已被映射到医学临床术语系统化命名法(SNOMED CT)。SNOMED CT作为一个中间术语,提供语义,在预先协调的临床概念和不同粒度级别的临床前概念的组合之间架起桥梁。从临床术语到SNOMED CT的映射是从现有资源中获取的,而从临床前术语到SNOMED CT的映射是手动创建的。协调模板定义了可以为映射探索的关系类型,并分配了反映映射不精确的惩罚分数。使用Rosetta Stone语义方法和词法术语匹配方法对60个预先协调的概念子集进行映射。这两个结果都是手动评估的。结果:临床前术语(组织病理学术语,非临床数据交换标准(SEND)代码列表,小鼠成年大体解剖本体)和临床术语(MedDRA)中的34,308个概念被映射到SNOMED CT作为中间桥接术语。已经开发了一个术语服务,它可以动态返回临床前和临床概念之间的精确和不精确映射。在评估集上,来自术语服务的映射的精度很高(95%),远高于词法术语匹配的精度(22%)。结论:罗塞塔石碑方法使用语义丰富的中间术语来映射预先协调的临床概念和不同程度准确性的临床前概念组合。生成精确和不精确映射的可能性允许将大量的临床前和临床数据关联起来,这在翻译用例中是有帮助的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mapping between clinical and preclinical terminologies: eTRANSAFE's Rosetta stone approach.

Mapping between clinical and preclinical terminologies: eTRANSAFE's Rosetta stone approach.

Mapping between clinical and preclinical terminologies: eTRANSAFE's Rosetta stone approach.

Mapping between clinical and preclinical terminologies: eTRANSAFE's Rosetta stone approach.

Background: The eTRANSAFE project developed tools that support translational research. One of the challenges in this project was to combine preclinical and clinical data, which are coded with different terminologies and granularities, and are expressed as single pre-coordinated, clinical concepts and as combinations of preclinical concepts from different terminologies. This study develops and evaluates the Rosetta Stone approach, which maps combinations of preclinical concepts to clinical, pre-coordinated concepts, allowing for different levels of exactness of mappings.

Methods: Concepts from preclinical and clinical terminologies used in eTRANSAFE have been mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). SNOMED CT acts as an intermediary terminology that provides the semantics to bridge between pre-coordinated clinical concepts and combinations of preclinical concepts with different levels of granularity. The mappings from clinical terminologies to SNOMED CT were taken from existing resources, while mappings from the preclinical terminologies to SNOMED CT were manually created. A coordination template defines the relation types that can be explored for a mapping and assigns a penalty score that reflects the inexactness of the mapping. A subset of 60 pre-coordinated concepts was mapped both with the Rosetta Stone semantic approach and with a lexical term matching approach. Both results were manually evaluated.

Results: A total of 34,308 concepts from preclinical terminologies (Histopathology terminology, Standard for Exchange of Nonclinical Data (SEND) code lists, Mouse Adult Gross Anatomy Ontology) and a clinical terminology (MedDRA) were mapped to SNOMED CT as the intermediary bridging terminology. A terminology service has been developed that returns dynamically the exact and inexact mappings between preclinical and clinical concepts. On the evaluation set, the precision of the mappings from the terminology service was high (95%), much higher than for lexical term matching (22%).

Conclusion: The Rosetta Stone approach uses a semantically rich intermediate terminology to map between pre-coordinated clinical concepts and a combination of preclinical concepts with different levels of exactness. The possibility to generate not only exact but also inexact mappings allows to relate larger amounts of preclinical and clinical data, which can be helpful in translational use cases.

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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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