IDEAS home Printed from https://ideas.repec.org/a/ids/ijbisy/v13y2013i2p151-172.html
   My bibliography  Save this article

Predicting re-hospitalisations using intelligent systems: an exploratory study

Author

Listed:
  • John W. Showalter
  • Girish H. Subramanian

Abstract

Intelligent systems and knowledge management are shown to have wide ranging applications for businesses. In healthcare, applications of expert systems and knowledge management have been popular since the use of MYCIN in the 1970s. In this research, we study the use of intelligent systems in the prediction of re-hospitalisations. A Bayesian network, an artificial neural network using multilayer perception, an artificial neural network using radial basis function, a support vector machine and an expert system (five intelligent systems) were used in the prediction. All the models except the support vector machine were able to provide good prediction of re-hospitalisation (with AUC > 0.6) and complete risk stratification for re-hospitalisation within 30 days of discharge with data available at admission, showing great promise for future application of intelligent systems in healthcare.

Suggested Citation

  • John W. Showalter & Girish H. Subramanian, 2013. "Predicting re-hospitalisations using intelligent systems: an exploratory study," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 13(2), pages 151-172.
    • Handle: RePEc:ids:ijbisy:v:13:y:2013:i:2:p:151-172
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=54333
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijbisy:v:13:y:2013:i:2:p:151-172. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=172 .

    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.