A Collaborative Approach to Machine Learning in Healthcare and Life Sciences
It's generally accepted that you need a team with a wide variety of skills to build modern machine learning (ML) pipelines and make them operational. But what does that team look like, and how do they work together? These questions are especially important when the skills required are specialized. When you’re developing interactive clinical and healthcare applications (backed by specialized statistical methods and made to scale on terabytes of highly complex data), you want the right people by your side.
In this latest webinar with Data Science Central, we'll describe three healthcare and life sciences projects in which business analysts, data scientists, and ML engineers collaborated. These applications and use cases are applicable across a number of industries.
Tune in to learn about the latest advances in big data analytics and artificial intelligence (AI) from PerkinElmer. And see real-life examples of how TIBCO data science and analytics solutions combined with the PerkinElmer AI platform can be used to create:
- An imaging-based phenotypic screening of cell-based disease models using high-content screening (HCS)
- Clinical translational systems designed to detect and score significant biomarkers in clinical prognostication
Also, find out how, in just three days, a diverse team from TIBCO responded to a critical healthcare challenge, analyzing health outcomes using socioeconomic data from countries around the world.
Sean Welch, Host and Producer, Data Science Central