BUSCADOR RECOLECTA

BackgroundFAIR (findability, accessibility, interoperability, and reusability) guidingprinciples seek the reuse of data and other digital research input, output, and objects(algorithms, tools, and workflows that led to that data) making themfindable, accessible,interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governedinitiative-defined a seven-step FAIRificationprocessfocusingondata,butalsoindicatingtherequired work for metadata. This FAIRification process aims at addressing the translation ofraw datasets into FAIR datasets in a general way, without considering specific requirementsand challenges that may arise when dealing with some particular types of data., This work was performed in the scope of FAIR4Healthproject. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovationprogramme under grant agreement number 824666.

[Background] FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data., [Objectives] This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by “GO FAIR” which addresses the identified gaps that such process has when dealing with health datasets., [Methods] A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing., [Results] A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow., [Discussion] Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets., [Conclusion] Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture., This work was performed in the scope of FAIR4Health project31. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666.

[EN]: The digital era, that we are living nowadays, is transforming health, health care models and services, and the role of society in this new reality. We currently have a large amount of stored health data, including clinical, biometric, and scientific research data. Nonetheless, its potential is not being fully exploited. It is essential to foster the sharing and reuse of this data not only in research but also towards the development of health technologies in order to improve health care efficiency, as well as products, services or digital health apps, to promote preventive and individualized medicine and to empower citizens in health literacy and self-management. In this sense, the FAIR concept has emerged, which implies that health data is findable, accessible, shared and reusable, facilitating interoperability between systems, ensuring the protection of personal and sensitive data. In this paper we review the FAIR concept, ‘FAIRification’ process, FAIR data versus open access data, ethical issues and the general data protection regulation, and digital health and citizen science., [PT]: Vivemos uma nova era digital que está a transformar a saúde, os modelos de cuidados e serviços de saúde, e o próprio papel da sociedade nesta realidade. Atualmente dispomos de uma grande quantidade de dados de saúde armazenados, incluindo dados clínicos, biométricos e de investigação científica, cuja potencialidade não está a ser devidamente explorada. É essencial favorecer a partilha e reutilização destes dados não só na investigação, como também para o desenvolvimento de tecnologias para melhorar a eficiência dos cuidados de saúde, de produtos ou serviços de saúde digitais, promover uma medicina preventiva e individualizada, mas também o empoderamento da população em literacia em saúde e na gestão da doença. Recentemente, surgiu o conceito FAIR que implica que os dados de saúde sejam facilmente localizáveis, acessíveis, partilhados e reutilizáveis, facilitando desta forma a interoperacionalidade entre sistemas e assegurando a proteção de dados pessoais e sensíveis. Neste artigo é feita uma revisão do conceito FAIR, processo de ‘FAIRificação’, dados FAIR versus dados de acesso livre, questões de éticas e o regulamento geral de proteção de dados, e saúde digital e ciência cidadã., Este trabalho foi financiado pelo Projeto Europeu FAIR4Health (EU H2020/No 824666) e pela FCT/MCTES através de financiamento público (UIDB/04378/2020).

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

[Links/relationships to ancillary data sets] To generate these metadata, datasets with health care or research data from SAS, IACS, UNIGE, UCSC, UP was used., [Relationship between files]
- SAS CapabilityStatement, SAS DocumentManifest, SAS Provenance; was generated in SAS facilities.
- IACS CapabilityStatement, IACS DocumentManifest, IACS Provenance; was generated in IACS facilities.
- UNIGE CapabilityStatement, UNIGE DocumentManifest, UNIGE Provenance; was generated in UNIGE facilities.
- UCSC CapabilityStatement, UCSC DocumentManifest, UCSC Provenance; was generated UCSC SAS facilities.
- UP CapabilityStatement, UP DocumentManifest, UP Provenance; was generated in UP facilities., [Description of methods used for collection/generation of data] The FAIRification workflow designed by the FAIR4Health project, detailed here: https://doi.org/10.1055/s-0040-1713684 - http://hdl.handle.net/10261/236308., [Standards and calibration information] The files have JSON format, and are HL7 FHIR compliance., [Usage Licenses/restrictions placed on the data]
- SAS, IACS. CC BY-NC (Attribution-NonCommercial). https://creativecommons.org/licenses/by-nc/4.0/
- UNIGE, UCSC. CC BY-SA (Attribution-ShareAlike). https://creativecommons.org/licenses/by-sa/4.0/
- UP. CC BY-NC-ND (Attribution-NonCommercial-NoDerivs). https://creativecommons.org/licenses/by-nc-nd/4.0/, This repository contains the metadata of the FAIR4Health project which was generated during the FAIRification processes of the clinical partners. According to the FAIR4Health Common Data Model, the FAIRification process results in FAIRified datasets which are managed by an onFHIR Repository. FAIR4Health Common Data Model utilizes the profiling approach of HL7 FHIR to ensure a satisfactory level of FAIR principles for health research datasets. Each clinical partner of the FAIR4Health project applied the FAIRification Workflow using the FAIR4Health software and the associated metadata was generated automatically at each onFHIR repository. Under each folder, the respective data owner's metadata is presented with FHIR's DocumentManifest, Provenance and CapabilityStatement resources., This work was supported by the FAIR4Health project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666., File List: - SAS CapabilityStatement - SAS DocumentManifest - SAS Provenance - IACS CapabilityStatement - IACS DocumentManifest - IACS Provenance - UNIGE CapabilityStatement - UNIGE DocumentManifest - UNIGE Provenance - UCSC CapabilityStatement - UCSC DocumentManifest - UCSC Provenance - UP CapabilityStatement - UP DocumentManifest - UP Provenance, Peer reviewed

Studies in Health Technology and Informatics., The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method. We applied a series of questionnaires to a group of experts not only in different roles and skills, but also from various parts of Europe. Overall, 26 questions were formulated for 16 participants. The results showed that the users are satisfied with the capabilities and performance of the tool. The feedbacks were considered as recommendations for technical improvement and fed back into the software development cycle of the Data Curation Tool., This work was performed in the framework of FAIR4Health project. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666.

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance., This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). 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’., Peer reviewed

[Background] Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers., [Objective] The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD)., [Methods] The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies., [Results] Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases., [Conclusions] Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles., This work was supported by the FAIR4Health project [10], which received funding from the European Union’s Horizon 2020 research and innovation program under grant 824666. This research has also been cosupported by the Carlos III National Institute of Health through the Programa de Ciencia de Datos de la Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología program (IMPaCT-Data, 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 cofunded by the European Regional Development Fund Fondo Europeo de Desarrollo Regional “A way of making Europe.”, Peer reviewed

[Background] Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard., [Objective] Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers., [Methods] Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions., [Results] Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable., [Conclusions] We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks., This work was supported by the FAIR4Health project, which has received funding from the European Union Horizon 2020 research and innovation program under grant agreement 824666., Peer reviewed

[Background] The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes., [Objective] This paper aims to describe the analysis of the impact on health research management outcomes of the FAIR4Health solution., [Methods] To analyse the impact on health research management outcomes in terms of time and economic savings, a survey was designed and sent to experts on data management with expertise in the use of the FAIR4Health solution. Then, differences between the time and costs needed to perform the techniques with (i) standalone research, and (ii) using the proposed solution, were analyzed., [Results] In the context of the health research management outcomes, the survey analysis concluded that 56.57% of the time and 16800 EUR per month could be saved if the FAIR4Health solution is used., [Conclusions] Adopting principles in health research through the FAIR4Health solution saves time and, consequently, costs in the execution of research involving data management techniques., This work was co-supported by the FAIR4Health project [3], which 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 program (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’., Peer reviewed

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle., The work of A. Jacobsen, C. Evelo, M. Thompson, R. Cornet, R. Kaliyaperuma and M. Roos is supported by funding from the European Union’s Horizon 2020 research and innovation program under the EJP RD COFUND-EJP N° 825575. The work of A. Jacobsen, C. Evelo, C. Goble, M. Thompson, N. Juty, R. Hooft, M. Roos, S-A. Sansone, P. McQuilton, P. Rocca-Serra and D. Batista is supported by funding from ELIXIR EXCELERATE, H2020 grant agreement number 676559. R. Hooft was further funded by NL NWO NRGWI. obrug.2018.009. N. Juty and C. Goble were funded by CORBEL (H2020 grant agreement 654248). N. Juty, C. Goble, S-A. Sansone, P. McQuilton, P. Rocca-Serra and D. Batista were funded by FAIRplus (IMI grant agreement 802750). N. Juty, C. Goble, M. Thompson, M. Roos, S-A. Sansone, P. McQuilton, P. Rocca-Serra and D. Batista were funded by EOSClife H2020-EU (grant agreement number 824087). C. Goble was funded by DMMCore (BBSRC BB/M013189/). M. Thompson, M. Roos received funding from NWO (VWData 400.17.605). S-A. Sansone, P. McQuilton, P. Rocca-Serra and D. Batista have been funded by grants awarded to S-A. Sansone from the UK BBSRC and Research Councils (BB/L024101/1; BB/L005069/1), EU (H2020-EU 634107; H2020-EU 654241, IMI (IMPRiND 116060), NIH Data Common Fund, and from the Wellcome Trust (ISA-InterMine 212930/Z/18/Z; FAIRsharing 208381/A/17/Z). The work of A. Waagmeester has been funded by grant award number GM089820 from the National Institutes of Health. M. Kersloot was funded by the European Regional Development Fund (KVW-00163). The work of N. Meyers was funded by the National Science Foundation (OAC 1839030). The work of M.D. Wilkinson is funded by Isaac Peral/Marie Curie cofund with the Universidad Politécnica de Madrid and the Ministerio de Economía y Competitividad grant number TIN2014-55993-RM. The work of B. Magagna, E. Schultes, L. da Silva Santos and K. Jeffery is funded by the H2020-EU 824068. The work of B. Magagna, E. Schultes and L. da Silva Santos is funded by the GO FAIR ISCO grant of the Dutch Ministry of Science and Culture. The work of G. Guizzardi is supported by the OCEAN Project (FUB). M. Courtot received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. R. Cornet was further funded by FAIR4Health (H2020-EU grant agreement number 824666). K. Jeffery received funding from EPOS-IP H2020-EU agreement 676564 and ENVRIplus H2020-EU agreement 654182., Peer reviewed

© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)., [Objective] This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets., [Materials and methods] Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets., [Results] Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%., [Discussion] The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data., [Conclusion] This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements., This work was supported by the FAIR4Health project [35], which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Additionally, this study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PT20/00088 and IMP/00019 and co-funded by the European Union., Peer reviewed