Applying MCRDR to a multidisciplinary domain

Authors:
Ivan Bindoff;Byeong Ho Kang;Tristan Ling;Peter Tenni;Gregory Peterson
Affiliations:
University of Tasmania, School of Computing;University of Tasmania, School of Computing;University of Tasmania, School of Computing;University of Tasmania, Unit for Medical Outcomes and Research Evaluations;University of Tasmania, Unit for Medical Outcomes and Research Evaluations
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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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.