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