Bella, Gabor

Das, Subhashis

Elliott, Liz

Giunchiglia, Fausto

Pavis, Stephen

Robertson, David

Turra, Ettore

English

As medical research becomes ever finer-grained, experiments require healthcare data in quantities that single countries cannot provide. Cross-jurisdictional data collection remains, however, extremely challenging due to the diverging legal, professional, linguistic, normative, and technological contexts of the participating countries. Medical data heterogeneity, in particular, is still a largely unsolved problem on the international level, due to the complexity of data combined with strict precision and data protection constraints. We propose a scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology. Medical knowledge that drives the scalable integration process is divided into multiple functional layers and is maintained in a distributed manner across participating countries. We successfully applied the approach in the context of a research experiment across Scotland and Italy, and are currently adapting it within other initiatives of Europe-wide health data interoperability

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Cross-Border Medical Research Using Multi-Layered and Distributed Knowledge

https://www.research.ed.ac.uk/portal/files/189730440/Cross_Border_Medical_BELLA_DOA15012020_VOR_CC_BY_NC.pdf

Cross-Border Medical Research Using Multi-Layered and Distributed Knowledge

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