Data Element Mapping in the Data Privacy Era
Language
EN
Chapitre d'ouvrage
This item was published in
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
English Abstract
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 ...Read more >
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.Read less <
English Keywords
LOINC
Data element
Machine learning
Mapping.