IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0188532.html
   My bibliography  Save this article

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

Author

Listed:
  • Sumanth Swaminathan
  • Klajdi Qirko
  • Ted Smith
  • Ethan Corcoran
  • Nicholas G Wysham
  • Gaurav Bazaz
  • George Kappel
  • Anthony N Gerber

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.

Suggested Citation

  • Sumanth Swaminathan & Klajdi Qirko & Ted Smith & Ethan Corcoran & Nicholas G Wysham & Gaurav Bazaz & George Kappel & Anthony N Gerber, 2017. "A machine learning approach to triaging patients with chronic obstructive pulmonary disease," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
    • Handle: RePEc:plo:pone00:0188532
    DOI: 10.1371/journal.pone.0188532
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188532
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0188532&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0188532?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Douglas Spangler & Thomas Hermansson & David Smekal & Hans Blomberg, 2019. "A validation of machine learning-based risk scores in the prehospital setting," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-18, December.
    2. Adam Ceney & Stephanie Tolond & Andrzej Glowinski & Ben Marks & Simon Swift & Tom Palser, 2021. "Accuracy of online symptom checkers and the potential impact on service utilisation," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-16, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0188532. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.