AI for Diabetes Prediction & Prevention
Recent activities
- A Network meeting was held on May 28, 2024 bringing together community partners and Persons with Lived or Living Experience (PWLLE) from Peel Region to exchange perspectives, co-develop a community engagement approach, and gather insights to inform the development of the RAIHS framework and dashboard.
- A Network meeting was held on November 28, 2023, bringing together the Solution Network team, experts, and provider- and policy-level stakeholders to share perspectives and feedback on the RAIHS framework and discuss needs for diabetes prevention and management in Peel Region.
Can machine learning models be deployed in a socially responsible way to help predict and prevent type 2 diabetes in a population?
Preventing the onset and consequences of diabetes is a top priority for governments and health systems around the world. By 2030, almost 14 million Canadians will have either diabetes or pre-diabetes, which is estimated to incur almost $5 billion in direct costs to the health system.
Because of systems-level barriers such as socioeconomic disparities, poor access to care, high medication costs, lack of access to healthy and affordable foods, and the built environment, it has been difficult to scale effective diabetes prevention and management programs from the individual patient to entire populations. Populations at the intersection of these barriers are less likely to meet disease control targets across the continuum of diagnosis, engagement, and treatment, which leads to what has been termed a “cascade of care.”
Dismantling barriers to preventing the onset and consequences of diabetes across populations requires concerted efforts by governments, health system planners, and care providers. However, these efforts are hampered by the lack of analytic tools which can accurately identify the population-wide risk of developing diabetes and diabetes-related complications.
This Solution Network will focus on the socially responsible deployment of a set of already-validated machine learning-based risk prediction algorithms, working with community-level health system decision-makers in the region of Peel.
Meet the CIFAR Solution Network Members:
Ibukun Abejirinde
Solution Network Member
Pan-Canadian AI Strategy
University of Toronto
Jennifer Gibson
Solution Network Member
Pan-Canadian AI Strategy
Shion Guha
Solution Network Member
Pan-Canadian AI Strategy
Lorraine Lipscombe
Solution Network Member
Pan-Canadian AI Strategy
Women's College Hospital
Laura Rosella
Solution Network Co-Director
Pan-Canadian AI Strategy
James Shaw
Solution Network Co-Director
Pan-Canadian AI Strategy