Publicación Artículo científico (article).

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

Digital.CSIC. Repositorio Institucional del CSIC
Digital.CSIC. Repositorio Institucional del CSIC
  • Carmona-Pírez, Jonás
  • Poblador-Plou, Beatriz
  • Poncel-Falcó, Antonio
  • Rochat, Jessica
  • Álvarez-Romero, Celia
  • Martínez-García, Alicia
  • Angioletti, Carmen
  • Almada, Marta
  • Gencturk, Mert
  • Sinaci, A. Anil
  • Ternero Vega, Jara Eloísa
  • Gaudet-Blavignac, Christophe
  • Lovis, Christian
  • Liperoti, Rosa
  • Costa, Elisio
  • Parra-Calderón, Carlos Luis
  • Moreno-Juste, Aida
  • Gimeno-Miguel, Antonio
  • Prados-Torres, Alexandra
This article belongs to the Special Issue Addressing the Growing Burden of Chronic Diseases and Multimorbidity: Characterization and Interventions, 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., This study was performed in the framework of FAIR4Health, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’, and by REDISSEC (RD16/0001/0005) and RICAPPS (RD21/0016/0019) from Carlos III National Institute of Health. This work was also supported by Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health [Río Hortega Program, grant number CM19/00164]., Peer reviewed

Digital.CSIC. Repositorio Institucional del CSIC

Digital.CSIC. Repositorio Institucional del CSIC
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Digital.CSIC. Repositorio Institucional del CSIC