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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4805

Title: CASESIAN: a knowledge-based system using statistical and experiential perspectives for improving the knowledge sharing in the medical prescription process
Authors: Ting, S. L.
Kwok, Siu Keung
Tsang, Albert H. C.
Lee, W. B.
Subjects: Bayesian theorem
Case-based reasoning
Knowledge-based system
Knowledge sharing
Medical prescription
Issue Date: Jul-2010
Publisher: Elsevier
Citation: Expert systems with applications, July 2010, v. 37, no. 7, p. 5336-5346.
Abstract: Knowledge sharing is crucial for better patient care in the healthcare industry, but it is challenging for physicians to exchange their clinical insights and practice experiences, particularly with regard to the issuing of prescriptions for medicine. The aim of our study is to facilitate knowledge sharing and information exchange in this area by means of a knowledge-based system. We propose a knowledge-based system, CASESIAN, to automatically model each physician’s prescription experience. This is done by collecting as many as possible instances of when the physician has issued a prescription. These occasions will be analyzed from a statistical perspective to form a reciprocal interactive knowledge sharing process for the issuing of medical prescriptions which we will call the prescription process. With the help of the prescription data in medical organizations, the knowledge-based system employs the Bayesian Theorem to correlate the experience of peers in order to evaluate individual prescription knowledge as retrieved through the case-based reasoning technique. In addition, a system prototype was implemented in a Hong Kong medical organization to evaluate the feasibility of such an approach. Our evaluation indicates that there is a significant improvement in knowledge sharing after the adoption of the system. CASESIAN obtains a higher rating in both recall and precision measurement when compared to traditional knowledge-based system. In particular, its information retrieval is much stronger than the baseline in around 40%. Furthermore, regarding the result of the interviews, physicians agree that the system can improve the storing and sharing of medical prescription knowledge.
Description: DOI: 10.1016/j.eswa.2010.01.023
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 7, (July 2010), DOI: 10.1016/j.eswa.2010.01.023
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/4805
ISSN: 0957-4174
Appears in Collections:ISE Journal/Magazine Articles

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