The projects, to be completed within one year, include research and applications in machine learning to generate synthetic datasets from real-world datasets.
They explore different artificial intelligence methods to generate medical images and clinical data that aim to ensure fidelity and patient privacy.
The project led by Professor Gagné involves the generation of privacy-preserving synthetic data from administrative databases on prescription drug consumption for the analysis of drug use in the Quebec population. The initiative is carried out in partnership with the Régie de l’assurance maladie du Québec (RAMQ). Our research members Anne-Sophie Charest and Philippe Després are also part of the project team.
The other projects supported by the fund are led by teams from the Université du Québec à Montréal (UQÀM) and the University of British Columbia.
Read the press release on the CIFAR website