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Machine Learning in Pharmacovigilance

Explore how machine learning is used within the regulatory and pharmacovigilance landscape with this on-demand course.


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


  • Since machine learning requires resources from across the organization, this course is designed for anyone interested in sponsoring or joining a machine learning project within their organization.

    • 学習目的

      • 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