Advertisement

Explaining Anomalous Responses to Treatment in the Intensive Care Unit

  • Laura Moss
  • Derek Sleeman
  • Malcolm Booth
  • Malcolm Daniel
  • Lyndsay Donaldson
  • Charlotte Gilhooly
  • Martin Hughes
  • Malcolm Sim
  • John Kinsella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

The Intensive Care Unit (ICU) provides treatment to critically ill patients. When a patient does not respond as expected to such treatment it can be challenging for clinicians, especially junior clinicians, as they may not have the relevant experience to understand the patient’s anomalous response. Datasets for 10 patients from Glasgow Royal Infirmary’s ICU have been made available to us. We asked several ICU clinicians to review these datasets and to suggest sequences which include anomalous or unusual reactions to treatment. Further, we then asked two ICU clinicians if they agreed with their colleagues’ assessments, and if they did to provide possible explanations for these anomalous sequences. Subsequently we have developed a system which is able to replicate the clinicians’ explanations based on the knowledge contained in its several ontologies; further the system can suggest additional explanations which will be evaluated by the senior consultant.

Keywords

Intensive Care Unit Senior Consultant Anomalous Data Temporal Abstraction Case Base Reasoning System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Heindl, B., Schmidt, R., Schmid, G., Haller, M., Pfaller, P., Gierl, L., Pollwein, B.: A case-based consiliarius for therapy recommendation(ICONS):computer-based advice for calculated antibiotic therapy in intensive care medicine. Computer Methods and Programs in Biomedicine 52, 117–127 (1997)CrossRefPubMedGoogle Scholar
  3. 3.
    Chinn, C.A., Brewer, W.F.: An Empirical Test of a Taxonomy of Responses to Anomalous Data in Science. Journal of Research in Science Teaching 35, 623–654 (1998)CrossRefGoogle Scholar
  4. 4.
    Kuhn, D.: The structure of scientific revolutions. University of Chicago Press (1962)Google Scholar
  5. 5.
    Alberdi, E., Sleeman, D., Korpi, M.: Accommodating Surprise in Taxonomic Tasks: The Role of Expertise. Cognitive Science 24, 53–91 (2000)CrossRefGoogle Scholar
  6. 6.
    Anders Ericsson, K., Simon, H.A.: Protocol analysis: verbal reports as data. MIT Press, Cambridge (1993)Google Scholar
  7. 7.
    Walker, M.G., Wiederhold, G.: Acquisition and Validation of Knowledge from Data. Intelligent Systems, 415–428 (1990)Google Scholar
  8. 8.
    Miksch, S., Horn, W., Popow, C., Paky, F.: VIE-VENT: knowledge-based monitoring and therapy planning of the artificial ventilation of newborn infants. Artificial Intelligence in Medicine 10, 218–229 (1993)Google Scholar
  9. 9.
    Patel, V.L., Zhang, J., Yoskowitz, N.A., Green, R., Sayan, O.R.: Translational cognition for decision support in critical care environments: A review. Journal of Biomedical Informatics 41, 413–431 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Bridewell, W., Buchanan, B.G.: Extracting Plausible Explanations of Anomalous Data. Technical reportGoogle Scholar
  11. 11.
    Shahar, Y., Musen, M.A.: Knowledge-Based Temporal Abstraction in Clinical Domains. Artificial Intelligence in Medicine 8, 267–298 (1996)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Laura Moss
    • 1
  • Derek Sleeman
    • 1
  • Malcolm Booth
    • 2
  • Malcolm Daniel
    • 2
  • Lyndsay Donaldson
    • 2
  • Charlotte Gilhooly
    • 2
  • Martin Hughes
    • 2
  • Malcolm Sim
    • 2
  • John Kinsella
    • 2
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenScotland
  2. 2.University Section of Anaesthesia, Pain & Critical Care Medicine, University of GlasgowGlasgow

Personalised recommendations