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.
Cross-Border Medical Research using Multi-Layered and Distributed Knowledge / Bella, Gábor; Elliot, Liz; Das, Subhashis; Pavis, Stephen; Turra, Ettore; Robertson, David; Giunchiglia, Fausto. - 325:(2020), pp. 2956-2963. (Intervento presentato al convegno PAIS 2020, included in ECAI 2020 tenutosi a Santiago de Compostela, Spain nel 29th August–8th September 2020) [10.3233/FAIA200469].
Cross-Border Medical Research using Multi-Layered and Distributed Knowledge
Bella, Gábor;Das, Subhashis;Giunchiglia, Fausto
2020-01-01
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
PAIS_Cross_Border_Medical_Research___PREPRINT.pdf
Solo gestori archivio
Tipologia:
Post-print referato (Refereed author’s manuscript)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
524.47 kB
Formato
Adobe PDF
|
524.47 kB | Adobe PDF | Visualizza/Apri |
FAIA-325-FAIA200469.pdf
accesso aperto
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Creative commons
Dimensione
771.09 kB
Formato
Adobe PDF
|
771.09 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione