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Journal of Healthcare Engineering
Volume 2018, Article ID 7174803, 10 pages
Research Article

An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes

1Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, MD, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
3Department of Emergency Medicine, Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA

Correspondence should be addressed to Sean Barnes; ude.dmu.htimshr@senrabs

Received 26 August 2017; Accepted 31 January 2018; Published 18 March 2018

Academic Editor: Weide Chang

Copyright © 2018 Sean Barnes et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Widespread adoption of electronic health records (EHR) and objectives for meaningful use have increased opportunities for data-driven predictive applications in healthcare. These decision support applications are often fueled by large-scale, heterogeneous, and multilevel (i.e., defined at hierarchical levels of specificity) patient data that challenge the development of predictive models. Our objective is to develop and evaluate an approach for optimally specifying multilevel patient data for prediction problems. We present a general evolutionary computational framework to optimally specify multilevel data to predict individual patient outcomes. We evaluate this method for both flattening (single level) and retaining the hierarchical predictor structure (multiple levels) using data collected to predict critical outcomes for emergency department patients across five populations. We find that the performance of both the flattened and hierarchical predictor structures in predicting critical outcomes for emergency department patients improve upon the baseline models for which only a single level of predictor—either more general or more specific—is used (). Our framework for optimizing the specificity of multilevel data improves upon more traditional single-level predictor structures and can readily be adapted to similar problems in healthcare and other domains.