Generation of synthetic health data: a project by Christian Gagné, Philippe Després and Anne-Sophie Charest supported by the CIFAR Catalyst program
Four collaborative research projects aimed at synthesizing health data have been selected to receive nearly $400,000 in funding following a symposium on synthetic health data co-organized by CIFAR, IVADO and Mila. Among them is a project led by our director Christian Gagné, professor at the Faculty of Science and Engineering of Laval University.
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.
Quotes
Elissa Strome, Senior Director, Pan-Canadian AI Strategy, CIFAR
“Collaboration across sectors, whether researchers in academia or working in the private sector or healthcare institutions, is absolutely essential to create innovative, effective and transformative AI solutions to improve diagnostics and patient care.”
Catherine Saine, Director, Partnerships and Strategy, Mila
“By bringing together academic and industry researchers to explore the opportunities and challenges of using and implementing synthetic health data, we are paving the way for new uses of AI to improve patient care.”
Barbara Decelle, Health Research Advisor, IVADO
“AI algorithms and intelligent systems hold great promise in the health sector. Facilitating access to data while preserving privacy is essential to developing robust and accountable algorithms and respecting the will of citizens.”
Lilia Jemai, National Director, Artificial Intelligence, Mitacs
“We look forward to seeing the results of these innovative solutions that use synthetic data to preserve privacy, while enabling AI to have the large volume of data needed to accelerate solution discovery in healthcare.”
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