Machine Learning in Pharmacovigilance

This on-demand course will explore machine learning (ML) within the Regulatory/ Pharmacovigilance (PV) landscape. The instructors will provide a high-level introduction to machine learning, including common tools and project tips.  Example applications, such as evaluation of Single Case Drug-Event-Pair (DEP) causality using the Modified Naranjo Causality Score for ICSRs (MONARCSi) will be reviewed and evaluated. The course will also focus on important non-technical aspects of using ML in PV, including potential approaches to performance evaluation, monitoring over time, maintaining human oversight, reporting, and legal considerations.

This on-demand course takes an average of 3.75 hours to complete. Learners have access to the course for one year from the date of purchase.

• Introduction to machine learning and artificial intelligence
• Looking deeper into machine learning
• A simple machine learning example
• Building your human expert reference comparator
• Artificial intelligence (AI) for Individual Case Safety Report (ICSR) processing and assessment: Lessons learned and a framework for readiness
• MHRA opinion on the use of machine learning in pharmacovigilance

At the conclusion of this activity, participants should be able to:

• Recognize key recent advances making Machine Learning in pharmacovigilance practical   
• Identify potential use cases in pharmacovigilance 
• Assess the potential benefits, limitations, and risks of Machine Learning applied to pharmacovigilance