Programmatic Theme: Clinical Research Informatics

Abstract: Asthma is a prevalent chronic respiratory condition, and acute exacerbations represent a significant fraction of the economic and health-related costs associated with asthma. We present results from a novel study that is focused on modeling asthma exacerbations from data contained in patients’ electronic health records. This work makes the following contributions: (i) we develop an algorithm to phenotype asthma exacerbations from EHRs (AUC = 0.77), (ii) we determine that models learned via supervised learning approaches can predict asthma exacerbations in the near future, and (iii) we develop an approach, based on mixtures of semi-Markov models, that is able to identify subpopulations of asthma patients sharing distinct temporal and seasonal patterns in their exacerbation susceptibility.

Learning Objective: The audience will understand how machine-learning methods can be applied to electronic health records in order to predict asthma exacerbations in individual patients, and identify subpopulations of patients who share distinct temporal and seasonal patterns in their exacerbation susceptibility.

Authors:

Alexander Cobian, University of Wisconsin
Madeline Abbott, University of Michigan
Akshay Sood, University of Wisconsin
Yuriy Sverchkov, University of Wisconsin
Lawrence Hanrahan, University of Wisconsin
Theresa Guilbert, Cincinnati Children's Hospital
Mark Craven (Presenter)
University of Wisconsin