Leveraging Data Across Time and Space to Build Predictive Models for Healthcare-Associated Infections

Presenter: Jenna Wiens, Electrical Engineering and Computer Science, Massachusetts Institute of Technology

The proliferation of electronic medical records holds out the promise of using machine learning and data mining to build models that will help healthcare providers improve patient outcomes. However, building useful models from these datasets presents many technical problems. The task is made challenging by the large number of factors, both intrinsic and extrinsic, influencing a patient¿s risk of an adverse outcome, the inherent evolution of that risk over time, and the relative rarity of adverse outcomes. In this talk, I will describe the development and validation of hospital-specific models for predicting healthcare-associated infections (HAIs), one of the top-ten contributors to death in the US. I will show how by adapting techniques from time-series classification, transfer learning and multi-task learning one can learn a more accurate model for patient risk stratification for the HAI Clostridium difficile (C. diff). Jenna Wiens is a Ph.D. Candidate in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). She holds an S.M. degree in EECS from MIT.

Date: Wednesday, Mar 19, 2014
Time: 11:30 am - 12:30 pm
Where: Gross Hall 330
Contact: Currin, Ellen
Phone: 660-5252
Email: ecurrin@ee.duke.edu