Faculty Candidate Seminar|
Leveraging Data Across Time and Space to Build Predictive Models for Healthcare-Associated Infections
Monday, February 10, 2014|
4:00pm - 5:00pm
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About the Event
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.
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. She is interested in solving the technical challenges that arise when considering the practical application of machine learning in medicine. In addition to her work on predicting healthcare associated infections she has applied machine-learning methods to the automated interpretation of electrocardiograms and the extraction of strategically useful information from player tracking data in the NBA.
Open to: Public