Overview

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.

Featured topics

- 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

Who should attend?

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.

Learning objectives

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

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Machine Learning in Pharmacovigilance

Overview

Featured topics

Who should attend?

Learning objectives

Be informed and stay engaged.