Applying MCRDR to a Multidisciplinary Domain

  • Ivan Bindoff
  • Byeong Ho Kang
  • Tristan Ling
  • Peter Tenni
  • Gregory Peterson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4830)

Abstract

This paper details updated results concerning an implementation of a Multiple Classification Ripple Down Rules (MCRDR) system which can be used to provide quality Decision Support Services to pharmacists practicing medication reviews (MRs), particularly for high risk patients. The system was trained on 126 genuine cases by an expert in the field; over the course of 19 hours the system had learned 268 rules and was considered to encompass over 80% of the domain. Furthermore, the system was found able to improve the quality and consistency of the medication review reports produced, as it was shown that there was a high incidence of missed classifications under normal conditions, which were repaired by the system automatically. However, shortcomings were identified including an inability to handle absent data, and shortcomings concerning standardization in the domain, proposals to solve these shortcomings are included.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ivan Bindoff
    • 1
  • Byeong Ho Kang
    • 1
  • Tristan Ling
    • 1
  • Peter Tenni
    • 2
  • Gregory Peterson
    • 2
  1. 1.University of Tasmania, School of Computing 
  2. 2.University of Tasmania, Unit for Medical Outcomes and Research Evaluations 

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