TY - JOUR
T1 - FAIR4Health
T2 - Findable, Accessible, Interoperable and Reusable data to foster Health Research
AU - Alvarez-Romero, Celia
AU - Martínez-García, Alicia
AU - Sinaci, A. Anil
AU - Gencturk, Mert
AU - Méndez, Eva
AU - Hernández-Pérez, Tony
AU - Liperoti, Rosa
AU - Angioletti, Carmen
AU - Löbe, Matthias
AU - Ganapathy, Nagarajan
AU - Deserno, Thomas M.
AU - Almada, Marta
AU - Costa, Elisio
AU - Chronaki, Catherine
AU - Cangioli, Giorgio
AU - Cornet, Ronald
AU - Poblador-Plou, Beatriz
AU - Carmona-Pírez, Jonás
AU - Gimeno-Miguel, Antonio
AU - Poncel-Falcó, Antonio
AU - Prados-Torres, Alexandra
AU - Kovacevic, Tomi
AU - Zaric, Bojan
AU - Bokan, Darijo
AU - Hromis, Sanja
AU - Djekic Malbasa, Jelena
AU - Rapallo Fernández, Carlos
AU - Velázquez Fernández, Teresa
AU - Rochat, Jessica
AU - Gaudet-Blavignac, Christophe
AU - Lovis, Christian
AU - Weber, Patrick
AU - Quintero, Miriam
AU - Perez-Perez, Manuel M.
AU - Ashley, Kevin
AU - Horton, Laurence
AU - Parra Calderón, Carlos Luis
N1 - Publisher Copyright: © 2022 Alvarez-Romero C et al.
PY - 2022
Y1 - 2022
N2 - Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.
AB - Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.
KW - Data reuse
KW - Data sharing
KW - FAIR principles
KW - HL7 FHIR
KW - Health data
KW - Health research
KW - Health research data management
KW - Machine learning
KW - Open science
KW - Privacy-preserving computing
UR - http://www.scopus.com/inward/record.url?scp=85133820721&partnerID=8YFLogxK
U2 - https://doi.org/10.12688/openreseurope.14349.2
DO - https://doi.org/10.12688/openreseurope.14349.2
M3 - Article
C2 - 37645268
SN - 2732-5121
VL - 2
JO - Open Research Europe
JF - Open Research Europe
M1 - 34
ER -