Machine Learning in Healthcare with David Kale

“Building a model to predict disease and deploying that in the wild – the bar for success is much higher there than, say, deciding what ad to show you.”

Diagnosing illness today requires the trained eye of a doctor. With machine learning, we might someday be able to diagnose illness using only a data set. Today on Software Engineering Daily, we are joined by David Kale, a researcher at the intersection of machine learning and clinical data. We discuss the machine learning and research techniques he is using to diagnose illnesses using neural networks, and we also talk about the challenges of performing data science in hospitals, where the data is mostly confidential. David will also be presenting at Strata + Hadoop World in San Jose. We’re partnering with O’Reilly to support this conference – if you want to go to Strata, you can save 20% off a ticket with our code PCSED.

Questions

• What kind of work does a data scientist at a children’s hospital do?
• Where is machine learning actually improving healthcare?
• What types of data are present in the intensive care unit?
• Can you give me an example of how you used an LSTM to make a prediction?
• What were the results of your recurrent neural network experiments?
• Do you think that deep learning is overhyped right now?

Links

• Learning to Diagnose with LSTM Recurrent Neural Networks
• Strata+Hadoop World
• Lasagne
• Theano
• Torch
• Deep Learning for Java
• Recurrent neural network
• David’s research page
  

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