JUCS - Journal of Universal Computer Science 14(16): 2720-2736, doi: 10.3217/jucs-014-16-2720
Intelligent Decision Support in Medicine: back to Bayes?
Gitte Lindgaard‡, Catherine Pyper‡, Monique Frize‡, Robin Walker§, Craig Boutilier|, Bowen Hui|, Sheila Narasimhan‡, Janette Folkens‡, Bill Winogron¶, Peter Egan¶, Colin Jones¶‡ Carleton University, Ottawa, Canada§ IWK Health Centre, Halifax, Canada| University of Toronto, Toronto, Canada¶ S4Potential, Almonte, Canada
Corresponding author: Gitte Lindgaard ( gitte_lindgaard@carleton.ca ) © Gitte Lindgaard, Catherine Pyper, Monique Frize, Robin Walker, Craig Boutilier, Bowen Hui, Sheila Narasimhan, Janette Folkens, Bill Winogron, Peter Egan, Colin Jones. This article is freely available under the J.UCS Open Content License. Citation:
Lindgaard G, Pyper C, Frize M, Walker R, Boutilier C, Hui B, Narasimhan S, Folkens J, Winogron B, Egan P, Jones C (2008) Intelligent Decision Support in Medicine: back to Bayes? JUCS - Journal of Universal Computer Science 14(16): 2720-2736. https://doi.org/10.3217/jucs-014-16-2720 |  |
Abstract
Decision Support Systems are proliferating rapidly in many areas of human endeavour including clinical medicine and psychology. While these are typically based on rule-based systems, decision trees, or Artificial Neural Networks, this paper argues that Bayes Theorem can be applied fruitfully to support expert decisions both in dynamically changing situations requiring the system progressively to adapt, and when this is not the case. One example of each of these two types is given. One provides diagnostic support for human decision makers; the other, an e-health mental intervention system provides decision rules enabling it to respond and provide the most appropriate training modules to input from clients with changing needs. The contributions of psychological research underlying both systems is summarized.
Keywords
Bayes' Theorem, Decision Support Systems (DSS), diagnostic error, individuating information, base rates, e-health intervention