从语法到语义互操作性 在瘤学领域使用超本科学
Mirna El Ghosh1, Varvara Kalokyri2, Mélanie Sambres1
1Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, LIMICS, Paris, France.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究引入了超本科,以实现各种癌症成像数据的语义互操作性,解决医疗保健数据整合的挑战. 这种方法可以在分布式存储库中增强数据共享和分析.
科学领域:
- 生物医学信息学 生物医学信息学
- 健康 数据科学 数据科学
- 本体学 工程学 工程学
背景情况:
- 由于语法和语义异质性,医疗保健数据集成面临着挑战.
- 像OMOP和FHIR这样的现有标准解决了语法互操作性,但不是语义复杂性.
- 语义互操作性对于在瘤学中利用分布式大数据至关重要.
研究的目的:
- 为癌症成像数据的语义互操作性提出一个本体学的方法.
- 支持EUCAIM项目,通过实现跨异质存储库的数据集成.
- 为瘤学领域利用一个语义上有充分依据的超神经学.
主要方法:
- 为瘤学领域量身定制的超神经病学开发.
- 在癌症图像数据模型中应用本体学来解决语义异质性.
- 在EUCAIM项目框架内整合本体论方法.
主要成果:
- 证明了使用Hyperontology用于语义互操作性的可行性.
- 促进了分布式大数据存储库与异质癌症图像数据的集成.
- 建立了对瘤学数据的语义统一方法.
结论:
- 提出的本体学方法有效地支持异质癌症成像数据的语义互操作性.
- 这种方法提高了EUCAIM项目的数据集成和分析能力.
- 在复杂的医疗保健大数据中克服语义差异时,本体学至关重要.
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