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      Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory

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          Abstract

          The coming-into-force of the EU General Data Protection Regulation (GDPR) is a watershed moment in the legal recognition of enforceable rights to informational self-determination. The rapid evolution of legal requirements applicable to data use, however, has the potential to outstrip the capabilities of networks of biomedical data users to respond to the shifting norms. It can also delegitimate established institutional bodies that are responsible for assessing and authorising the downstream use of data, including research ethics committees and institutional data custodians. These burdens are especially pronounced for clinical and research networks that are of transnational scale, because the legal compliance burden for outbound international data transfers from the EEA is especially high. Legislatures, courts, and regulators in the EU should therefore implement the following three legal changes. First, the responsibilities of particular actors in a data sharing network should be delimited through the contractual allocation of responsibilities between collaborators. Second, the use of data through secure data processing environments should not trigger the international transfer provisions of the GDPR. Third, the use of federated data analysis methodologies that do not provide analysis nodes or downstream users access to identifiable personal data as part of the outputs of those analyses should not be considered circumstances of joint controllership, nor lead to the users of non-identifiable data to be considered controllers or processors. These small clarifications of, or modifications to, the GDPR would facilitate the exchange of biomedical data amongst clinicians and researchers.

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          The future of digital health with federated learning

          Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
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            Swarm Learning for decentralized and confidential clinical machine learning

            Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1 , 2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation 4 , 5 . Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. Swarm Learning is a decentralized machine learning approach that outperforms classifiers developed at individual sites for COVID-19 and other diseases while preserving confidentiality and privacy.
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              DataSHIELD: taking the analysis to the data, not the data to the analysis

              Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.
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                Author and article information

                Contributors
                alexander.bernier@mcgill.ca
                Journal
                Eur J Hum Genet
                Eur J Hum Genet
                European Journal of Human Genetics
                Springer International Publishing (Cham )
                1018-4813
                1476-5438
                15 June 2023
                15 June 2023
                : 1-8
                Affiliations
                [1 ]EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
                [2 ]euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
                [3 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Centre of Genomics and Policy, , McGill University Faculty of Medicine and Health Sciences, ; Montréal, QC Canada
                [4 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Heidelberg Academy of Sciences and Humanities, , Heidelberg University, ; Heidelberg, Germany
                [5 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, , Faculty of Medicine, KU Leuven, ; Leuven, Belgium
                [6 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Institute on Ethics & Policy for Innovation (IEPI), , McMaster University, ; Hamilton, ON Canada
                [7 ]RECODID: Reconciliation of Cohort Data in Infectious Diseases, Heidelberg, Germany
                [8 ]GRID grid.450509.d, ELSI Services & Research, , BBMRI-ERIC, ; Graz, Austria
                [9 ]CINECA: Common Infrastructure for International Cohorts in Europe, Canada, and Africa, Heidelberg, Germany
                [10 ]EUCAN-Connect: Federated, FAIR Platform Enabling Large-Scale Analysis of High-Value Cohort Data Connecting Europe and Canada in Personalized Health, Groningen, the Netherlands
                [11 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, School of Social and Political Studies, , University of Glasgow, ; Glasgow, Scotland UK
                [12 ]EuCanImage: A European Cancer Image Platform Linked to Biological and Health Data for Next Generation Artificial Intelligence and Precision Medicine in Oncology, Barcelona, Spain
                [13 ]GRID grid.11480.3c, ISNI 0000000121671098, Social and Legal Sciences Applied to the New Technosciences Research Group, Faculty of Law, , University of the Basque Country, ; Bilbao, Spain
                [14 ]GRID grid.15781.3a, ISNI 0000 0001 0723 035X, CERPOP, Inserm, , Toulouse Paul Sabatier University, ; Toulouse, France
                [15 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Metamedica, Faculty of Law and Criminology, , Ghent University, ; Ghent, Belgium
                [16 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, Institute of Population Health, , University of Liverpool, ; Liverpool, UK
                [17 ]Lynkeus S.R.L, Roma, Italy
                [18 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Heidelberg Institute for Global Health, , Heidelberg University, ; Im Neuenheimer Feld 130/3, 69120 Heidelberg, Germany
                Author information
                http://orcid.org/0000-0001-8615-8375
                http://orcid.org/0000-0001-7004-2722
                http://orcid.org/0000-0002-4931-9560
                http://orcid.org/0009-0006-2923-1576
                http://orcid.org/0000-0002-3909-8071
                http://orcid.org/0000-0003-3813-8462
                http://orcid.org/0000-0001-7128-0474
                http://orcid.org/0000-0003-2294-593X
                http://orcid.org/0000-0002-0777-2092
                Article
                1403
                10.1038/s41431-023-01403-y
                10267538
                37322132
                a7f98553-cea8-41d5-bd69-58e53fe6ce6a
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 November 2022
                : 26 January 2023
                : 24 May 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000024, Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada);
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Categories
                Article

                Genetics
                social sciences,ethics
                Genetics
                social sciences, ethics

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