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 putbig data at the service of clinicians and administrators managing COVID-19 through point-of-care decision tools.
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
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.
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.
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
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 Guillaume Plourde, MD, PhD, CHUM¹
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