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