Towards a Semantic Data Harmonization Federated Infrastructure

Abstract

Data integration is an increasing need in medical informatics projects like the EU Precise4Q project, in which multidisciplinary semantically and syntactically heterogeneous data across several institutions needs to be integrated. Besides, data sharing agreements often allow a virtual data integration only, because data cannot leave the source repository. We propose a data harmonization infrastructure in which data is virtually integrated by sharing a semantically rich common data representation that allows their homogeneous querying. This common data model integrates content from well-known biomedical ontologies like SNOMED CT by using the BTL2 upper level ontology, and is imported into a graph database. We successfully integrated three datasets and made some test queries showing the feasibility of the approach.

The use of Foundational Ontologies in Bioinformatics

“…• Ontology-based federated data analysis: foundational ontologies were used in the development of common semantic data models for integrating heterogeneous data spread across different sources. The ontology-based federated approach allows data to be (re)used without leaving its source (e.g., [49]). Answers to the question related to the claimed advantages of foundational ontologies (RQ2 : "What are the claimed advantages of using foundational ontologies in bioinformatics?")…”

Advancements and Challenges in Data Harmonization: A Comprehensive Review

“…The need for advanced technological infrastructure to manage, analyze, and secure large volumes of data further underscores the challenges of data harmonization. Developing and maintaining the necessary technological frameworks requires significant investment and expertise, posing a barrier to organizations with limited resources [11].…”

“…Addressing semantic differences across data sources is essential for achieving effective data integration. Establishing a common understanding of data categories, terminologies, and meanings is crucial for ensuring that data from diverse sources can be accurately interpreted and utilized in a harmonized manner [11].…”

Semantic interoperability assessment : iShare framework

“…However, the reluctance to adopt a comprehensive EHR solution is also very prevalent, and one of the primary reasons for this reluctance is the inability of the EHRs to interlink and communicate with each other due to the lack of a comprehensive data standard that facilitates the exchange of data using a common data model (Bowles et al, 2013). In Europe, on the other hand, shared clinical terminologies and ontologies, as means to enable the faithful exchange of the meaning of information, have been addressed in the EU Semantic Health interoperability roadmap (Martínez-Costa et al, 2014).…”

“…example, the architecture proposed by Martínez-Costa et al is organized in five layers and spans from heterogeneous data repositories to homogeneous and semantically explicit representations(Martínez- Costa et al, 2014). Its first layer (structure heterogeneous data) comprises structured clinical data, which may be physically stored within an EHR repository and accessed via an interface conforming to some standard like HL7 CDA, openEHR, EN ISO 13606, or to a proprietary database schema.…”