Through an established partnership with 9 Canadian COVID treatment centres,
we are building a large repository of anonymized, multi-modality data sampled from
patients with confirmed or suspected COVID-19.
Using this data, our team of leading experts in clinical research and health data science
is developing models to put big data at the service of clinicians and
administrators managing COVID-19 through point-of-care decision tools.
There is a pressing need to develop tools that can help physicians diagnose COVID-19 rapidly, determine if different disease presentations warrant different types of treatment, flag patients at high risk of deteriorating, and ensure healthcare resources are attributed efficiently and equitably.
Through an established partnership with 8 hospital sites in Québec and 1 site in Ontario, we have already built a large database of biological signal data from patients with COVID-19. We are developing a collaborative data analysis infrastructure to pool data from multiple sites while minimizing the exchange of patient-level information, distributed and federated learning techniques. Risk prediction models will be developed to identify patients at high risk of COVID prior to the availability of definitive testing, characterize the trajectories of patients presenting different manifestations of the disease, identify patients at high risk of clinical deterioration, and make forecasts to plan hospital resources and staffing. The accuracy of predictions will be continuously verified using new cases, which will be identified in real time at participating hospital sites.
These predictive models will be used to build tools that can help physicians better treat patients with COVID-19, and provide actionable recommendations to support Canada's response to COVID-19.
Help physicians diagnose COVID-19 rapidly by building point-of-care tools that leverage machine learning models at the bedside.
Analyze disease phenotypes to assess whether different disease presentations warrant different treatment approaches.
Early warning system
Find warning signs that can alert clinicians to imminent patient deterioration, enabling pre-emptive treatment and monitoring.
Health resource usage
Forecast the need for beds, ventilators, and optimize the provision of healthcare services for patients with and without COVID-19.
Michaël Chassé, MD, PhD, CHUM¹
David Buckeridge, MD, PhD, McGill University Health Centre²
Jonathan Afilalo, MD, MSc, Jewish General Hospital²
Han Ting Wang MD, MSc, Hôpital Maisonneuve-Rosemont¹
Yiorgos A. Cavayas, MD, MSc, Hôpital Sacré-Coeur de Montréal¹
Alexis Turgeon MD, MSc, CHU de Québec³
Patrick Archambault, MD, MSc, CISSS Chaudière-Appalaches³
Joelle Pineau, PhD, Mila institute²
Louis-Antoine Mullie, MD, CHUM¹
Guillaume Plourde, MD, PhD, CHUM¹
Marc Afilalo, MD, JGH²
François M. Carrier, MD, MSc, CHUM¹
Emmanuel Charbonney, MD, PhD¹
Carl C.-Lefebvre, MD, MSc, CHUM¹
Joseph Paul Cohen, PhD, Mila¹
Audrey Durand, PhD, Mila³
Madeleine Durand, MD, MSc¹
Philippe Jouvet, MD, PhD, MBA, HSJ¹
Esli Osmanlliu MD, MSc (cand), MCH²
Shane W. English, MD, MSc, OH⁴
François Lamontagne, MD, MSc⁵
Brent Richards, MD, MSc, JGH²
Antony Robert, MD, MASc, MUHC²
Michaël Sauthier, MD, MBI, HSJ¹
Nicolas Sauthier, MD, CHUM¹
An Tang, MD, MSc, CHUM¹
¹ Université de Montréal • ² McGill University • ³ Université Laval • ⁴ University of Ottawa • ⁵ Université Sherbrooke
© CODA-19 investigators, 2020