Prediction models and longitudinal outcome analysis for child and youth psychiatric acute care admissions or access to high-intensity community-based services: machine learning models to identify risk factors, quantify service capacity short-falls, and promote appropriate transitions into the adult system of care

This research project aims to improve the care of children with severe mental health or substance use disorders by developing a ML model that can predict which children are most likely to need hospitalization. The project will analyze data from various services, including transitions into the adult mental health/addictions system and examine hospital admission outcomes using longitudinal data. By utilizing this model, healthcare providers can make better decisions on when to admit children to the hospital, ensuring that resources are used effectively. Island Health will conduct the research project, and the expected benefit is to improve the care provided to children with mental health and substance use disorders, as well as to better understand the transition into the adult mental health/addictions system. This initiative will contribute to meeting the priorities for Mental Health & Substance Use in Island Health, ultimately improving the health and well-being of these children.

Faculty Supervisor:

Amirali Baniasadi

Student:

Partner:

Vancouver Island Health Authority (Victoria, BC)

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Victoria

Program: