Which is More Reliable, Expert Experience or Information Itself? Weight Scheme of Complex Cases for Health Management Decision Making

Dongxiao Gu (), Changyong Liang (), Kyung-Sun Kim (), Changhui Yang (), Wenjuan Cheng () and Jun Wang ()
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Dongxiao Gu: School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China
Changyong Liang: School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China
Kyung-Sun Kim: School of Library and Information Studies, University of Wisconsin-Madison, 600 N. Park Street, Madison, WI 53706, USA
Changhui Yang: Engineering Research Center of Intelligent Decision-Making and Information System Technology of Ministry of Education of China, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China
Wenjuan Cheng: School of Computer and Information, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China
Jun Wang: Department of Computer Science, University of Wisconsin-Milwaukee, P.O. Box 784, Milwaukee, WI 53201, USA

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 03, 597-620

Abstract: How to obtain valuable knowledge more effectively from historical cases and satisfy the requirements of supporting diagnosis or management decision making is one of the important and challenging issues in the research field of modern historical information management and intelligent decision-making science. In this study, we develop a novel case-based reasoning (CBR) method which is based on information entropy and improved gray systems theory for knowledge acquisition of historical diagnosis decision-making cases. Specially, information entropy for weight determination is introduced into the CBR, as well as a gray system theory combined to support the diagnosis decision making of breast cancer. Based on two different real-world data sets, we conduct experimental studies to compare the performance of the Delphi method and information entropy. We also investigate which combination is best among different weight determination methods and retrieval algorithms. The results suggest that: generally, information entropy is a better approach to weight derivation and better matching effect can be obtained if it is integrated into the retrieval algorithm based on gray system theory rather than Euclidean distance algorithm. Our study can provide a novel approach to obtain weight values of cases, as well as an effective tool to mine valuable decision knowledge from historical cases in public hospitals.

Keywords: Health management decision making; weight determination; cased-based reasoning; case matching; breast cancer; information entropy; intelligent decision support systems; knowledge mining (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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DOI: 10.1142/S0219622014500424

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