Introduction to Machine Learning for Pharmacoepidemiologists

‎1. Course Aim ‎
The aim of this course is to provide background and guidance for pharmacoepidemiologists ‎around the use of machine learning (ML). This course will help attendees understand how ‎ML can be used in the context of pharmacoepidemiology, map terminology used in ML to ‎that commonly used in pharmacoepidemiology, and to review differences in the analytic ‎approach compared to traditional statistical modelling. Key categories of ML will be ‎reviewed with examples of algorithms for each. Finally, the course will cover basic data ‎preparation and tools required for ML analysis. An interactive exercise will be included to ‎reinforce key concepts and demonstrate practical applications of techniques discussed ‎through the course. The course faculty aims to promote engagement between the ‎pharmacoepidemiology community and ML/informatics community. ‎

‎2. Requisites Statement ‎
Level of expertise: entry level
‎●‎ This course will assume limited/no previous exposure to ML
‎●‎ This course is intended to help pharmacoepidemiologists have a basic understanding ‎of machine learning techniques to aid in the development of collaborative projects ‎
‎●‎ A basic understanding of epidemiology is required

‎3. Course Objectives‎
• To understand how and when ML can be used for pharmacoepidemiology research
• To learn common ML terminology and understand how these terms differ from ‎standard pharmacoepidemiology terminology
• To provide a high-level overview of the steps in a project involving ML, including ‎design considerations, data preparation, modelling, model validation, and ‎interpretation of results
• To review real-world examples of ML use in pharmacoepidemiologic research
• To explain the principles of NLP and discuss tips and tricks for getting the best out of ‎this approach
• To participate in an interactive exercise applying key considerations learned during ‎the course

‎4. Syllabus Outline‎
‎●‎ Introduction ‎
‎●‎ Session 1: The applications of machine learning in pharmacoepidemiology ‎
‎●‎ Session 2: When to use ML in pharmacoepidemiology
‎●‎ Session 3: Types of machine learning and associated algorithms ‎
‎●‎ Session 4: Supervised machine learning workflow: from data to deployment ‎
‎●‎ Session 5: Natural Language Processing (NLP) ‎
‎●‎ Session 6: De-mystifying deep learning ‎
‎●‎ Interactive session: Putting it all into practice ‎
‎●‎ Q&A