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dc.contributor.authorAlmeida, João Rafael
dc.contributor.authorPazos, A.
dc.contributor.authorOliveira, José Luís
dc.date.accessioned2024-07-01T16:07:54Z
dc.date.available2024-07-01T16:07:54Z
dc.date.issued2022
dc.identifier.citationAlmeida, J. R., Pazos, A., & Oliveira, J. L. (2022). BIcenter-AD: Harmonising Alzheimer’s Disease cohorts using a common ETL tool. Informatics in Medicine Unlocked, 35, 101133. https://doi.org/10.1016/j.imu.2022.101133es_ES
dc.identifier.issn2352-9148
dc.identifier.urihttp://hdl.handle.net/2183/37600
dc.description.abstract[Abstract]: Background: Many scientific studies have sought to obtain a better understanding of specific medical conditions. Concerning Alzheimer’s Disease, there is a lack of reliable diagnostics and this can be related to the availability of only small-scale ongoing biomarker studies and longitudinal cohorts including these subjects. Aiming to generate more substantial clinical evidence, researchers have started to perform multiple cohort analyses. While this is currently possible by harmonising these cohorts into a common data model, the migration pipelines are usually implemented using programming languages. Therefore, cohort owners may have difficulties contributing during the validation stage of these pipelines. Results: To reduce the dependency on technical teams’ support when validating the data transformations, it is proposed the use of an ETL tool with visual features. BIcenter is a collaborative web platform designed to implement ETL tasks through the browser. These pipelines are constructed using drag-and-drop features and intuitive forms to customise the ETL steps. This tool is an open-source project and is accessible at https://bioinformatics-ua.github.io/BIcenter-AD/. Conclusions: Our methodology produces interoperable cohorts for multicentric disease-specific studies. Therefore, the tool was validated using Alzheimer’s Disease cohorts from several countries, combining at the end 6,669 subjects and 172 medical attributes. The harmonised cohorts now enable multi-cohort querying and analysis, helping in the execution of new studies.es_ES
dc.description.sponsorshipThis works is funded by National Funds through the FCT — Foundation for Science and Technology, Portugal, in the context of the projects UIDB/00127/2020 and DSAIPA/AI/0088/2020. João Rafael Almeida is also funded by the FCT — Foundation for Science and Technology, Portugal , under the grant SFRH/BD/147837/2019. All authors approved the version of the manuscript to be published.es_ES
dc.description.sponsorshipPortugal. Fundação para a Ciência e a Tecnologia; SFRH/BD/147837/2019es_ES
dc.description.sponsorshipPortugal. Fundação para a Ciência e a Tecnologia; UIDB/00127/2020es_ES
dc.description.sponsorshipPortugal. Fundação para a Ciência e a Tecnologia; DSAIPA/AI/0088/2020es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttps://doi.org/10.1016/j.imu.2022.101133es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectClinical studieses_ES
dc.subjectData harmonisationes_ES
dc.subjectETLes_ES
dc.subjectOMOP CDMes_ES
dc.subjectAlzheimer’s diseasees_ES
dc.titleBIcenter-AD: Harmonising Alzheimer’s Disease cohorts using a common ETL tooles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInformatics in Medicine Unlockedes_ES
UDC.volume35es_ES
UDC.issue101133es_ES
UDC.startPage1es_ES
UDC.endPage7es_ES
dc.identifier.doi10.1016/j.imu.2022.101133


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