Data Element Mapping in the Data Privacy Era
dc.rights.license | open | en_US |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | GRIFFIER, Romain
IDREF: 252908562 | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | COSSIN, Sebastien
IDREF: 197817874 | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | KONSCHELLE, Francois | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | MOUGIN, Fleur
IDREF: 116242337 | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | JOUHET, Vianney | |
dc.date.accessioned | 2023-02-15T11:19:13Z | |
dc.date.available | 2023-02-15T11:19:13Z | |
dc.date.issued | 2022-05-25 | |
dc.identifier.isbn | 978-1-64368-284-6 (print) | 978-1-64368-285-3 (online) | en_US |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/171961 | |
dc.description.abstractEn | Secondary 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.iso | EN | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | Attribution-NonCommercial 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/us/ | * |
dc.source.title | Studies in Health Technology and Informatics | en_US |
dc.subject.en | LOINC | |
dc.subject.en | Data element | |
dc.subject.en | Machine learning | |
dc.subject.en | Mapping. | |
dc.title.en | Data Element Mapping in the Data Privacy Era | |
dc.title.alternative | Stud Health Technol Inform | en_US |
dc.type | Chapitre d'ouvrage | en_US |
dc.identifier.doi | 10.3233/SHTI220469 | en_US |
dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
dc.identifier.pubmed | 35612087 | en_US |
bordeaux.page | 332-336 | en_US |
bordeaux.volume | 294: Challenges of Trustable AI and Added-Value on Health | en_US |
bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
bordeaux.institution | Université de Bordeaux | en_US |
bordeaux.institution | INSERM | en_US |
bordeaux.team | AHEAD_BPH | en_US |
bordeaux.inpress | non | en_US |
hal.identifier | hal-03990404 | |
hal.version | 1 | |
hal.date.transferred | 2023-02-15T11:19:15Z | |
hal.export | true | |
dc.rights.cc | Pas de Licence CC | en_US |
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