Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, LIMICS, Paris, France.
Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece.
Stud Health Technol Inform. 2024 Aug 22;316:1385-1389. doi: 10.3233/SHTI240670.
Interoperability is crucial to overcoming various challenges of data integration in the healthcare domain. While OMOP and FHIR data standards handle syntactic heterogeneity among heterogeneous data sources, ontologies support semantic interoperability to overcome the complexity and disparity of healthcare data. This study proposes an ontological approach in the context of the EUCAIM project to support semantic interoperability among distributed big data repositories that have applied heterogeneous cancer image data models using a semantically well-founded Hyperontology for the oncology domain.
互操作性对于克服医疗保健领域数据集成的各种挑战至关重要。虽然 OMOP 和 FHIR 数据标准处理异类数据源之间的语法异构性，但本体支持语义互操作性，以克服医疗保健数据的复杂性和差异性。本研究在 EUCAIM 项目的背景下提出了一种本体方法，以支持使用语义基础良好的肿瘤学 Hyperontology 为异类癌症图像数据模型应用的分布式大数据存储库之间的语义互操作性。
