Big Data, AI, and Machine Learning in Drug Safety

Summary

In this five-day course, developed in collaboration with the University of Copenhagen, you gain knowledge and insights you can translate into action in your role as a pharmacovigilance professional responsible for taking advantage of the possibilities inherent in big data, AI, and machine learning. For today’s pharmacovigilance professionals, it is necessary to be capable of using the data science toolbox. But how do we choose the right data for our specific objective? And how do we collect and process data in a way that considers the ethical, legal, and regulatory aspects of big data and data science?

The number of signals is continuously increasing. This course provides an overview of AI and how technologies like this can be applied to better understand safety and adverse events. You’ll learn to use these tools effectively to benefit patients and enhance your studies.

A key focus is how to identify the most important signals—the relevant, actionable signals. With the volume of data available, it’s easy to feel overwhelmed. This course will help you distinguish between noise and true value.

You will gain a fundamental understanding of data science that will support you in making better-informed decisions based on comprehensive data analysis. In addition to theoretical insights, we will discuss practical examples, including best practices and common pitfalls.

The course begins with a two-week online component, equivalent to one day of work, followed by a 5-day in-person session.

Keywords

• Big data
• Artificial Intelligence
• Machine learning
• Drug safety
• Data science