RT Journal Article
T1 Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm.
A1 Carmona-Pirez, Jonas
A1 Poblador-Plou, Beatriz
A1 Poncel-Falco, Antonio
A1 Rochat, Jessica
A1 Alvarez-Romero, Celia
A1 Martinez-Garcia, Alicia
A1 Angioletti, Carmen
A1 Almada, Marta
A1 Gencturk, Mert
A1 Sinaci, A Anil
A1 Ternero-Vega, Jara Eloisa
A1 Gaudet-Blavignac, Christophe
A1 Lovis, Christian
A1 Liperoti, Rosa
A1 Costa, Elisio
A1 Parra-Calderon, Carlos Luis
A1 Moreno-Juste, Aida
A1 Gimeno-Miguel, Antonio
A1 Prados-Torres, Alexandra
K1 FAIR principles
K1 mortality
K1 multimorbidity
K1 pathfinder case study
K1 privacy-preserving distributed data mining
K1 research data management
AB The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.
PB MDPI AG
YR 2022
FD 2022-02-11
LK http://hdl.handle.net/10668/21032
UL http://hdl.handle.net/10668/21032
LA en
NO Carmona-Pírez J, Poblador-Plou B, Poncel-Falcó A, Rochat J, Alvarez-Romero C, Martínez-García A, et al. Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm. Int J Environ Res Public Health. 2022 Feb 11;19(4):2040.
DS RISalud
RD Apr 11, 2025