Abstract
Objective
The prediction of emergency department (ED) disposition at triage remains challenging.
Machine learning approaches may enhance prediction. We compared the performance of
several machine learning approaches for predicting two clinical outcomes (critical
care and hospitalization) among ED patients with asthma or COPD exacerbation.
Methods
Using the 2007–2015 National Hospital and Ambulatory Medical Care Survey (NHAMCS)
ED data, we identified adults with asthma or COPD exacerbation. In the training set
(70% random sample), using routinely-available triage data as predictors (e.g., demographics,
arrival mode, vital signs, chief complaint, comorbidities), we derived four machine
learning-based models: Lasso regression, random forest, boosting, and deep neural
network. In the test set (the remaining 30% of sample), we compared their prediction
ability against traditional logistic regression with Emergency Severity Index (ESI,
reference model).
Results
Of 3206 eligible ED visits, corresponding to weighted estimates of 13.9 million visits,
4% had critical care outcome and 26% had hospitalization outcome. For the critical
care prediction, the best performing approach– boosting – achieved the highest discriminative
ability (C-statistics 0.80 vs. 0.68), reclassification improvement (net reclassification
improvement [NRI] 53%, P = 0.002), and sensitivity (0.79 vs. 0.53) over the reference model. For the hospitalization
prediction, random forest provided the highest discriminative ability (C-statistics
0.83 vs. 0.64) reclassification improvement (NRI 92%, P < 0.001), and sensitivity (0.75 vs. 0.33). Results were generally consistent across
the asthma and COPD subgroups.
Conclusions
Based on nationally-representative ED data, machine learning approaches improved the
ability to predict disposition of patients with asthma or COPD exacerbation.
Keywords
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Article Info
Publication History
Published online: June 27, 2018
Accepted:
June 26,
2018
Received in revised form:
June 25,
2018
Received:
May 20,
2018
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
