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distributed analytics on Sensitive Medical Data:The Personal Health Train

Data Intelligence 2020年第1期2卷 96-107,305,306,307页

作者： Oya Beyan Ananya Choudhury Johan van Soest Oliver Kohlbacher Lukas Zimmermann Holger Stenzhorn Md.Rezaul Karim Michel Dumontier Stefan Decker Luiz Olavo Bonino da Silva Santos Andre Dekker Fraunhofer Institute for Applied Information Technology(FIT) 53754 Sankt AugustinGermany RWTH Aachen University 52056 AachenGermany Department of Radiation Oncology(MAASTRO) GROW School for Oncology and Developmental BiologyMaastricht University Medical Center6200 MD MaastrichtThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science University of TübingenTubingenBaden-Württemberg 72076Germany Quantitative Biology Center University of TübingenTubingenBaden-Württemberg 72076Germany Institute for Translational Bioinformatics University of TubingenTubingenBaden-Württemberg 72076Germany Center for Bioinformatics University of TübingenGermany GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands

In recent years,as newer technologies have evolved around the healthcare ecosystem,more and more data have been *** analytics could power the data collected from numerous sources,both from healthcare institutions,or generated by individuals themselves via apps and devices,and lead to innovations in treatment and diagnosis of diseases;improve the care given to the patient;and empower citizens to participate in the decision-making process regarding their own health and ***,the sensitive nature of the health data prohibits healthcare organizations from sharing the *** Personal Health Train(PHT)is a novel approach,aiming to establish a distributed data analytics infrastructure enabling the(re)use of distributed healthcare data,while data owners stay in control of their own *** main principle of the PHT is that data remain in their original location,and analytical tasks visit data sources and execute the *** PHT provides a distributed,flexible approach to use data in a network of participants,incorporating the FAIR *** facilitates the responsible use of sensitive and/or personal data by adopting international principles and *** paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.

关键词： distributed analytics Data reuse FAIR Health data Ethics and privacy

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DAMS:A distributed analytics Metadata Schema

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Data Intelligence 2021年第4期3卷 528-547页

作者： Sascha Welten Laurenz Neumann Yeliz Ucer Yediel Luiz Olavo Bonino da Silva Santos Stefan Decker Oya Beyan Chair Informatik 5 RWTH Aachen University52056 AachenGermany Fraunhofer Institute for Applied Information Techniques(FIT) 53757 Sankt AugustinGermany Faculty of Electrical Engineering Mathematics and Computer ScienceUniversity of Twente7500AE EnschedeThe Netherlands Department of Human Genetics Leiden University Medical CentreLeiden 2333 ZAThe Netherlands Institute of Medical Information Faculty of Medicine&University Hospital CologneUniversity of Cologne50674 CologneGermany

In recent years,implementations enabling distributed analytics(DA)have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the *** concepts propose privacy-enhancing alternatives to data centralisation approaches,which have restricted applicability in case of sensitive data due to ethical,legal or social ***,the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions,particularly the absence of basic metadata for data sets or analysis *** approach the mentioned problems,we propose a metadata schema for DA infrastructures,which provides a vocabulary to enrich the involved entities with descriptive *** initially perform a requirement analysis with domain experts to reveal necessary metadata items,which represents the foundation of our ***,we transform the obtained domain expert knowledge into user stories and derive the most significant semantic *** the final step,we enable machine-readability via RDF(S)and SHACL *** deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA ***,we evaluate the schema’s compliance with the FAIR *** evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR *** a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures,our work lowers data access and analysis *** represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

关键词： distributed analytics Federated analytics Personal Health Train Metadata schema RDF(S) SHACL FAIR

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