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Content provided by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
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Interpretability and Data Challenges with Duke University's Dr. Cynthia Rudin

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Manage episode 281313515 series 2833637
Content provided by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Our guest today is Dr. Cynthia Rudin, a professor of computer science, electrical and computer engineering, and statistical science at Duke University. She describes her work on data-driven risk prediction models, which have been validated in real Intensive Care Units and provide a simpler, effective alternative to neural nets. She also discusses the benefits of model interpretability and the challenges that researchers face when accessing medical data.

Pranav and Adriel first provide context for the interview, giving an overview of prognostic models, interpretability and black box models, and GDPR and CCPA, which starts at 13:05. If you like what you hear, let a friend know, subscribe wherever you get your podcasts, and connect with us on Twitter @AIHealthPodcast.

  continue reading

41 episodes

Artwork
iconShare
 
Manage episode 281313515 series 2833637
Content provided by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Our guest today is Dr. Cynthia Rudin, a professor of computer science, electrical and computer engineering, and statistical science at Duke University. She describes her work on data-driven risk prediction models, which have been validated in real Intensive Care Units and provide a simpler, effective alternative to neural nets. She also discusses the benefits of model interpretability and the challenges that researchers face when accessing medical data.

Pranav and Adriel first provide context for the interview, giving an overview of prognostic models, interpretability and black box models, and GDPR and CCPA, which starts at 13:05. If you like what you hear, let a friend know, subscribe wherever you get your podcasts, and connect with us on Twitter @AIHealthPodcast.

  continue reading

41 episodes

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