Data – Driving Recovery in the aftermath of COVID-19’s 1st Wave, Steadying for the 2nd

Numerous studies in the application of Machine Learning to mental health have demonstrated a range of benefits in the areas of diagnosis, treatment and support, research, and clinical administration. COVID-19 is an unprecedented health crisis causing a great deal of stress in populations in Canada. In this project, our aim is to apply practical machine learning approaches to study whether the effects of medical cannabis can help address anxiety, depression and sleep challenges exacerbated by COVID-19. Patient reported scores in the standardized medical questionnaires will be used to determine which specific cannabinoid therapy dosing regimens (Eg: High CBD versus Balanced CBD/THC versus High THC) are effective and, specifically, for which particular subsets of patients experiencing anxiety, depression and/or sleep challenges. The significance of this project is to advance the clinical delivery of precision-based cannabinoid medicine to address the SUA/MH (substance use and abuse/mental health) challenges COVID-19 is presently exacerbating.

Faculty Supervisor:

Sheela Ramanna

Student:

Negin Ashrafi

Partner:

Ekosi Health

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Winnipeg