Modeling Asthma Exacerbations from Electronic Health Records

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 for phenotyping asthma exacerbations from EHRs, (ii) we determine that models learned via supervised learning approaches can predict asthma exacerbations in the near future (AUC ≈ 0.77), and (iii) we develop an approach, based on mixtures of semi-Markov models, that is able to identify subpopula-tions of asthma patients sharing distinct temporal and seasonal patterns in their exacerbation susceptibility.