CODA-19: Collaborative Data Analysis to Improve
Clinical Care in Patients with COVID-19

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.

Supported by

Adapting to a global outbreak.

Helping clinicians on the frontline and ensuring efficient allocation of resources within healthcare institutions.

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.

Domains of Research

Rapid diagnosis

Help physicians diagnose COVID-19 rapidly by building point-of-care tools that leverage machine learning models at the bedside.

Clinical phenotypes

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.

Our Team and Partners

Our multi-disciplinary team combines leading technical expertise in the fields of machine learning, data science, epidemiology, and biostatistics, with strong clinical research expertise in radiology, internal medicine, emergency medicine, and critical care medicine.


Executive Committee

Michaël Chassé, MD, PhD, CHUM¹
David Buckeridge, MD, PhD, McGill University Health Centre²
Jonathan Afilalo, MD, MSc, Jewish General Hospital
²
Han Ting Wang MD, MSc, Hôpital Maisonneuve-Rosemont
¹
Yiorgos A. Cavayas, MD, MSc, Hôpital Sacré-Coeur de Montréal
¹
Alexis Turgeon MD, MSc, CHU de Québec
³
Patrick Archambault, MD, MSc, CISSS Chaudière-Appalaches
³


Joelle Pineau, PhD, Mila institute
²
Louis-Antoine Mullie, MD, CHUM¹
Guillaume Plourde, MD, PhD, CHUM¹

Steering Committee

Marc Afilalo, MD, JGH²
François M. Carrier, MD, MSc, CHUM
¹
Emmanuel Charbonney, MD, PhD
¹
Carl C.-Lefebvre, MD, MSc, CHUM
¹
Joseph Paul Cohen, PhD, Mila
¹

Audrey Durand, PhD, Mila³
Madeleine Durand, MD, MSc
¹
Philippe Jouvet, MD, PhD, MBA, HSJ
¹
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, HSJ
¹
Nicolas Sauthier, MD, CHUM
¹
An Tang, MD, MSc, CHUM
¹

¹ Université de Montréal ² McGill University ³ Université Laval University of Ottawa Université Sherbrooke

Contact Us

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