Applying Federated Machine Learning To Sensitive Healthcare Data At Rhino Health

Summary
A core challenge of machine learning systems is getting access to quality data. This often means centralizing information in a single system, but that is impractical in highly regulated industries, such as healthchare. To address this hurdle Rhino Health is building a platform for federated learning on health data, so that everyone can maintain data privacy while benefiting from AI capabilities. In this episode Ittai Dayan explains the barriers to ML in healthcare and how they have designed the Rhino platform to overcome them.
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
• Your host is Tobias Macey and today I'm interviewing Ittai Dayan about using federated learning at Rhino Health to bring AI capabilities to the tightly regulated healthcare industry

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what Rhino Health is and the story behind it?
• What is federated learning and what are the trade-offs that it introduces? 
  • What are the benefits to healthcare and pharmalogical organizations from using federated learning?
• What are some of the challenges that you face in validating that patient data is properly de-identified in the federated models?
• Can you describe what the Rhino Health platform offers and how it is implemented? 
  • How have the design and goals of the system changed since you started working on it?
• What are the technological capabilities that are needed for an organization to be able to start using Rhino Health to gain insights into their patient and clinical data? 
  • How have you approached the design of your product to reduce the effort to onboard new customers and solutions?
• What are some examples of the types of automation that you are able to provide to your customers? (e.g. medical diagnosis, radiology review, health outcome predictions, etc.)
• What are the ethical and regulatory challenges that you have had to address in the development of your platform?
• What are the most interesting, innovative, or unexpected ways that you have seen Rhino Health used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rhino Health?
• When is Rhino Health the wrong choice?
• What do you have planned for the future of Rhino Health?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

• Rhino Health
• Federated Learning
• Nvidia Clara
• Nvidia DGX
• Melloddy
• Flair NLP

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0