Applying machine learning to real-world data in rare disease

Using RWD and machine learning to future proof product planning

Spurred by regulatory incentives to develop treatments for rare and orphan diseases, the space has attracted enormous attention and is beginning to bear fruit. Nevertheless, the process of identifying, developing and delivering medicines to patients remains fraught with challenges. This whitepaper examines the dynamic role data might play in rare disease and personalized medicine and what companies need to utilize these tools and deliver needed medicines to patients.

This whitepaper will answer questions such as:

What is the state of rare disease diagnosis?
What are the regulatory considerations and guidelines associated with incorporating RWD?
What are the challenges of using machine learning with RWD for rare disease?
How can companies integrate ML and RWD into product planning to understand a patient population?
Case study: Building a ML model to help identify undiagnosed patients with a rare disease