Leveraging AI for Data Characterization and Analysis in a Youth Wellbeing Database in UK

This project aims to characterize and analyze the Community Health Dataset using AI techniques. This dataset, sourced from the Cambridge Community Services Trust, contains electronic health records (EHRs) of 111,317 children and young people aged 0–18 years, covering the period from 2007 to 2021. The significance of this project lies in several key aspects: (1) the large amount of available data, enabling the development of robust models and conclusions; (2) the broad age range of subjects, addressing a gap in research focused on youth mental health beyond student populations or controlled conditions; (3) the substantial number of samples related to diagnosed mental illnesses, such as ADHD and ASD, which remain underrepresented in the literature; and (4) the integration of AI tools to enhance data analysis. This analysis will provide key insights into healthcare system utilization and contribute to a better understanding of youth mental health in the UK, with the potential to apply these findings to similar databases in Canada.

Faculty Supervisor:

Thomas Doyle

Student:

Partner:

University of Cambridge

Discipline:

Computer science

Sector:

Education

University:

McMaster University

Program: