Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

Abstract:
        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.

DOI: 10.3390/ijerph19042040

Projects: FAIR4Health

Publication type: Journal article

Journal: International Journal of Environmental Research and Public Health

Human Diseases: No Human Disease specified

Citation: IJERPH 19(4):2040

Date Published: 1st Feb 2022

Registered Mode: by DOI

Authors: Jonás Carmona-Pírez, Beatriz Poblador-Plou, Antonio Poncel-Falcó, Jessica Rochat, Celia Alvarez-Romero, Alicia Martínez-García, Carmen Angioletti, Marta Almada, Mert Gencturk, A. Anil Sinaci, Jara Eloisa Ternero-Vega, Christophe Gaudet-Blavignac, Christian Lovis, Rosa Liperoti, Elisio Costa, Carlos Luis Parra-Calderón, Aida Moreno-Juste, Antonio Gimeno-Miguel, Alexandra Prados-Torres

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Carmona-Pírez, J., Poblador-Plou, B., Poncel-Falcó, A., Rochat, J., Alvarez-Romero, C., Martínez-García, A., Angioletti, C., Almada, M., Gencturk, M., Sinaci, A. A., Ternero-Vega, J. E., Gaudet-Blavignac, C., Lovis, C., Liperoti, R., Costa, E., Parra-Calderón, C. L., Moreno-Juste, A., Gimeno-Miguel, A., & Prados-Torres, A. (2022). Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 4, p. 2040). MDPI AG. https://doi.org/10.3390/ijerph19042040
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Created: 5th Apr 2022 at 07:51

Last updated: 5th Apr 2022 at 07:52

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