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Puskaric, M.* ; Attieh, H.A.* ; Prasser, F.* ; Gusinow, R. ; Dellacasa, C.* ; Rossi, E.* ; Naranjo, J.M.* ; Canziani, L.M.* ; Gorska, A.* ; Hasenauer, J.

Data infrastructure for integrating clinical data in the large-scale international ORCHESTRA cohort: From data import to federated analysis.

In: (Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024). 2024. 7998-8004 (Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024)
DOI
Large-scale international collaborations are increasingly managing large volumes of sensitive health data for research purposes. The use of such infrastructure requires fulfilling various technical and organizational requirements to ensure usability and security. Before data scientists can access the data, it must be imported into the infrastructure with high data security requirements. For federated analysis workflows, additional criteria, such as data harmonization and prevention of individual patient information disclosure, must also be met. This paper outlines the key components of the data infrastructure implemented in the European research project ORCHESTRA and elaborates on the methods that support federated analysis workflows in a heterogeneous legal environment. Special attention is given to data security, interoperability, and usability, from which data scientists and researchers would benefit. We demonstrate the usability of data infrastructure on a federated analysis and machine learning use cases on remote datasets which satisfies the previously mentioned requirements. Furthermore, we propose organizational measures to optimize the process, reducing the time between a data access request and granting access.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Clinical Research ; Data Security ; Federated Data Analysis ; Health Data
ISSN (print) / ISBN [9798350362480]
Konferenztitel Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
Quellenangaben Band: , Heft: , Seiten: 7998-8004 Artikelnummer: , Supplement: ,
Nichtpatentliteratur Publikationen
Institut(e) Institute of Computational Biology (ICB)