GRIFFIER, Romain; COSSIN, Sebastien; KONSCHELLE, Francois; MOUGIN, Fleur; JOUHET, Vianney

doi:10.3233/SHTI220469

Bordeaux population health [BPH]

COSSIN, Sebastien

Bordeaux population health [BPH]

KONSCHELLE, Francois
Bordeaux population health [BPH]

Langue

Chapitre d'ouvrage

Ce document a été publié dans

Studies in Health Technology and Informatics. 2022-05-25, vol. 294: Challenges of Trustable AI and Added-Value on Health, p. 332-336

IOS Press

Résumé en anglais

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.< Réduire

Mots clés en anglais

LOINC

Data element

Machine learning

Mapping.

URI

https://oskar-bordeaux.fr/handle/20.500.12278/171961

DOI

http://dx.doi.org/10.3233/SHTI220469

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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Data Element Mapping in the Data Privacy Era

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