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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGRIFFIER, Romain
IDREF: 252908562
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOSSIN, Sebastien
IDREF: 197817874
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorKONSCHELLE, Francois
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMOUGIN, Fleur
IDREF: 116242337
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJOUHET, Vianney
dc.date.accessioned2023-02-15T11:19:13Z
dc.date.available2023-02-15T11:19:13Z
dc.date.issued2022-05-25
dc.identifier.isbn978-1-64368-284-6 (print) | 978-1-64368-285-3 (online)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/171961
dc.description.abstractEnSecondary use of health data is made difficult in part because of large semantic heterogeneity. Many efforts are being made to align local terminologies with international standards. With increasing concerns about data privacy, we focused here on the use of machine learning methods to align biological data elements using aggregated features that could be shared as open data. A 3-step methodology (features engineering, blocking strategy and supervised learning) was proposed. The first results, although modest, are encouraging for the future development of this approach.
dc.language.isoENen_US
dc.publisherIOS Pressen_US
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.source.titleStudies in Health Technology and Informaticsen_US
dc.subject.enLOINC
dc.subject.enData element
dc.subject.enMachine learning
dc.subject.enMapping.
dc.title.enData Element Mapping in the Data Privacy Era
dc.title.alternativeStud Health Technol Informen_US
dc.typeChapitre d'ouvrageen_US
dc.identifier.doi10.3233/SHTI220469en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed35612087en_US
bordeaux.page332-336en_US
bordeaux.volume294: Challenges of Trustable AI and Added-Value on Healthen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.inpressnonen_US
hal.identifierhal-03990404
hal.version1
hal.date.transferred2023-02-15T11:19:15Z
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Studies%20in%20Health%20Technology%20and%20Informatics&rft.date=2022-05-25&rft.volume=294:%20Challenges%20of%20Trustable%20AI%20and%20Added-Value%20on%20Health&rft.spage=332-336&rft.epage=332-336&rft.au=GRIFFIER,%20Romain&COSSIN,%20Sebastien&KONSCHELLE,%20Francois&MOUGIN,%20Fleur&JOUHET,%20Vianney&rft.isbn=978-1-64368-284-6%20(print)%20%7C%20978-1-64368-285-3%20(online)&rft.genre=unknown


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