How to Use Machine Learning in Clinical Research Right Now

What You Will Learn

In machine learning, there are usually one of two distinct objectives: inference or prediction. In analyses involving clinical trial data and/or real-world data, a machine learning solution can be used by data scientists to detect patterns that conventional analysis and statistical methods cannot. Thus, a machine learning practitioner can help to infer which subgroups that respond, either particularly well or poorly, to a treatment. Similarly, machine learning algorithms can be used to predict a discrete or time-to-event outcome. When knowledge increases of subgroups of interest and on outcomes before they occur, the impact on patient care and clinical research is very powerful.

Attendees will learn how to:

• Why data science and the use of machine learning is essential to pharmaceutical, biotech, and medical device organizations
• How to use machine learning to understand outcomes before they occur
• Practical approaches for inference or prediction through machine learning
• Metrics to evaluate the performance of predictions made
• Possibilities for the future of machine learning in clinical research

Our Experts

• Richard Bryant, Senior Data Scientist
• Chris Hurley, Associate Director, Data Science