Using machine learning to allocate stratified care in an electronic cognitive behavioural therapy program for depression
Depression is a leading cause of disability worldwide, yet only one third of patients with depression receive care. This is because the traditional in-person format of mental health care delivery can be inaccessible, inefficient, and expensive. Electronic cognitive behavioural therapy (e-CBT) has been shown to be an effective solution to expand care access, efficiency, and affordability. Combining depression-based e-CBT with artificial intelligence, this study aims to develop an effective decision-making model that matches an individual’s needs with the right amount of care. Our goal at OPTT is to develop a platform that makes e-CBT more efficient and highly accessible to individuals facing mental health issues and this project will use artificial intelligence and tailored interventions to efficiently allocate mental health care resources. At a time when mental health challenges are on the rise due to the pandemic, this technique could scale up care capacity without sacrificing the quality of care.
View Full Project DescriptionNazanin Alavi
OPTT
Life Sciences
Health and Related Sciences & Technology; Information and cultural industries
Queen's University
Accelerate
