Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Machine learning for healthcare data

Katherine Heller (Duke University)
Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Hardcore Data Science, Healthcare
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Katherine Heller discusses multiple ways in which healthcare data is acquired and explains how machine learning methods are currently being introduced into clinical settings.

Topics include:

  • Modeling disease trends and other predictions, including joint predictions of multiple conditions, from electronic health record (EHR) data using Gaussian processes
  • Predicting surgical complications and transfer learning methods for combining databases
  • Using mobile apps and integrated sensors for improving the granularity of recorded health data for chronic conditions
  • Using a combination of mobile app and social network information in order to predict the spread of contagious disease
Photo of Katherine Heller

Katherine Heller

Duke University

Katherine Heller is an assistant professor in Duke University’s Departments of Statistical Science, Computer Science, and Electrical and Computer Engineering and at the Center for Cognitive Neuroscience, where she develops new methods and models to discover latent structure in data, including cluster structure, using Bayesian nonparametrics, hierarchical Bayes, time series techniques, and other Bayesian statistical methods, and applies these methods to problems in the brain and cognitive sciences, human social interactions, and clinical medicine. Previously, she was an NSF postdoctoral fellow in the Computational Cognitive Science Group at MIT and an EPSRC postdoctoral fellow at the University of Cambridge. Katherine has been the recipient of a first-round NSF BRAIN Initiative award, a Google faculty research award, and an NSF CAREER award. She holds a PhD from the Gatsby Unit at University College London.