Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4804
Title: RACER : Rule-Associated CasE-based Reasoning for supporting general practitioners in prescription making
Authors: Ting, SL
Wang, WM
Kwok, SK
Tsang, AHC
Lee, WB 
Keywords: Association rules mining
Case-based reasoning
Decision support
Hybrid intelligent system
Prescription
Issue Date: Dec-2010
Publisher: Elsevier
Source: Expert systems with applications, Dec. 2010, v. 37, no. 12, p. 8079-8089 How to cite?
Journal: Expert systems with applications 
Abstract: Prescription is an important element in the medical practice. An appropriate drug therapy is complex in which the decision of prescribing is influenced by many factors. Any discrepancy in the prescription making process can lead to serious consequences. In particular, the General Practitioners (GPs), who need to diagnose and treat a wide range of health conditions and diseases, must be knowledgeable enough in deciding what type of medicines should be given to the patients. With the widespread computerization of medical records, GPs now can make use of accumulated historic clinical data in retrieving similar decisions in therapeutic treatment for treating the new situation. However, the applications of decision support tools are rarely found in the prescription domain due to the complex nature of the domain and limitations of the existing tools. It was argued that existing tools can only solve a small amount of the cases on the real world dataset.
This paper proposes a new revised Case-based Reasoning (CBR) mechanism, named Rule-Associated CasE-based Reasoning (RACER), which integrates CBR and association rules mining for supporting GPs prescription. It aims at leveraging the two most common techniques in the field and dealing with the complex multiple values solution. Eight hundred real cases from a medical organization are collected and used for evaluating the performance of RACER. The proposed method was also compared with CBR and association rules mining for testing. The results demonstrate that the combination leads to increased in both recall and precision in various settings of parameters. The performance of RACER remains stable by using different sets of parameters, which shows that the most important element of the mechanism is self-determined.
URI: http://hdl.handle.net/10397/4804
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2010.05.080
Rights: Expert systems with applications © 2010 Elsevier B.V. All rights reserved. The journal web site is located at http://www.sciencedirect.com.
NOTICE: this is the author’s version of a work that was accepted for publication in Expert systems with applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert systems with applications , vol. 37, issue 12, (Dec. 2010), DOI: 10.1016/j.eswa.2010.05.080
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