Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Jonás Carmona-Pírez
* ORCID logo ,
Beatriz Poblador-Plou
ORCID logo ,
Antonio Poncel-Falcó
,
Jessica Rochat
,
Celia Alvarez-Romero
,
Alicia Martínez-García
ORCID logo ,
Carmen Angioletti
ORCID logo ,
Marta Almada
ORCID logo ,
Mert Gencturk
,
Anil Sinaci
,
Jara Eloisa Ternero-Vega
,
Christophe Gaudet-Blavignac
,
Christian Lovis
,
Rosa Liperoti
,
Elisio Costa
ORCID logo ,
Carlos Luis Parra-Calderón
,
Aida Moreno-Juste
ORCID logo ,
Antonio Gimeno-Miguel
,
Alexandra Prados-Torres
ORCID logo
Version 1 : Received: 13 January 2022 / Approved: 17 January 2022 / Online: 17 January 2022 (13:04:03 CET)

A peer-reviewed article of this Preprint also exists.

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. 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, 19, 2040. 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. 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, 19, 2040.

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.

Keywords

FAIR principles; Multimorbidity; Mortality; Research data management; Pathfinder case study; Privacy-Preserving Distributed Data Mining.

Subject

Public Health and Healthcare, Public Health and Health Services

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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