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

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.
ECAI 2020: 24th European Conference on Artificial Intelligence, Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) Proceedings
Amsterdam
IOS Press
978-1-64368-100-9
Bella, Gábor; Elliot, Liz; Das, Subhashis; Pavis, Stephen; Turra, Ettore; Robertson, David; Giunchiglia, Fausto
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].
File in questo prodotto:
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

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/263141
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact