Predicting population risk of suicide using health administrative data

Background: Suicide is a major international public health problem. To facilitate suicide prevention planning, mechanisms should be in place that enable policy and decision makers to make informed decisions and mobilize resources to high-risk populations at the right places, before tragic events occur. This vision requires us to shift the paradigm from predicting individual risk to predicting population risk of suicide. To realize this vision, we assemble an interdisciplinary innovation team that brings together policy and decision makers, computer scientists, IT experts, sociologists, health data scientists, mental health professionals, and people with lived experience. This novel interdisciplinary approach attempts to challenge current paradigms in suicide prevention, and if successful, will generate tangible health, economic and societal benefits for Canadians and beyond.



1) To develop machine learning (ML) algorithms for predicting population risk of suicide using province-wide health administrative data;

2) To develop highly usable visual tools for presenting data that can effectively inform health policy and decision makers;

3) To explore opportunities for integrating the risk prediction program into existing disease surveillance systems; and

4) To identify the barriers and facilitators to implementation, and explore the ethical and privacy issues of the prediction program.


Funding source: The New Frontier in Research Funds from the CIHR and SSHRC.


Team members: JianLi Wang (PI), Co-investigators: Alain Lesage, Geneviève Gariépy, Christian Gagné, Fatemeh Gholi Zadeh Kharrat, Jean-François Pelletier.

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