University of Pittsburgh

Using Bayesian Hierarchical Models to Predict Relevant Patient Data

graduate student
Date: 
Friday, March 8, 2019 - 12:30pm - 1:00pm

We are investigating the use of Bayesian hierarchical models (HM) to predict information-seeking behavior of physicians in electronic medical record (EMR) systems. Intensive care unit (ICU) physicians reviewed patient cases and identified patient data that were relevant in the context of a specific clinical task. Since each physician reviewed multiple cases, the annotations for these cases are not statistically independent. It is known that HMs are suitable in the situation where the independence assumption among data samples does not hold. I will introduce an ongoing research project on the development of a Learning EMR (LEMR) system that uses models of information-seeking behavior of physicians to draw a physician’s attention to the right data, at the right time for the right patient. I will present preliminary results of our experiments using Bayesian HMs models of information-seeking behavior. 

Slides: 

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