Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

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.

Journal reference: Int. J. Environ. Res. Public Health 2022, 19, 2040
DOI: 10.3390/ijerph19042040

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

MEDICINE & PHARMACOLOGY, General Medical Research

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