Dalla Lana School of Public Health

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In this focused presentation, we aim to demystify the complex world of machine learning (ML) and illuminate its potential to revolutionize public health research. Starting with a clear breakdown of essential ML terminologies, we will guide attendees through the processes involved in employing ML techniques for insightful predictive and causal analyses. Central to our workshop is a series of real-world examples from our own clinical research endeavors, showcasing the practical application of ML approaches in addressing public health challenges. These examples will cover disease areas such as Tuberculosis and Multiple Sclerosis. We will navigate through the hurdles commonly faced in this innovative field, sharing strategies devised from our experiences to overcome these obstacles. The session is designed to shed light on best practices, drawing upon specific methodologies to stimulate an enriching dialogue about their relative merits and applicability. Participants will have ample opportunities to ask questions.

Speakers

• Dr. Ehsan Karim, Assistant Professor, School of Population and Public Health at UBC & Scientist at the Centre for Advancing Health Outcomes
• Md. Belal Hossain, PhD Candidate at University of British Columbia’s School of Population and Public Health
• Hanna Frank, PhD Candidate, University of British Columbia’s School of Population and Public Health
• Momenul Haque Mondol, PhD Student, University of British Columbia’s School of Population and Public Health

Location

This session held virtually on zoom.