A machine learning approach to triaging patients with chronic obstructive pulmonary disease

Sumanth Swaminathan, Klajdi Qirko, Ted Smith, Ethan Corcoran, Nicholas G Wysham, Gaurav Bazaz, George Kappel and Anthony N Gerber

PLOS ONE, 2017, vol. 12, issue 11, 1-21

Abstract: COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient’s need for emergency care.

Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0188532

DOI: 10.1371/journal.pone.0188532

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