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

Author

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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)

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.

Suggested Citation

  • Dongxiao Gu & Changyong Liang & Kyung-Sun Kim & Changhui Yang & Wenjuan Cheng & Jun Wang, 2015. "Which is More Reliable, Expert Experience or Information Itself? Weight Scheme of Complex Cases for Health Management Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 597-620.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:03:n:s0219622014500424
    DOI: 10.1142/S0219622014500424
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    Citations

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    Cited by:

    1. Xiaoyan Jiang & Sai Wang & Jie Wang & Sainan Lyu & Martin Skitmore, 2020. "A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    2. Junchang Li & Jiantong Zhang & Ye Ding, 2020. "Uncertain Multiplicative Language Decision Method Based on Group Compromise Framework for Evaluation of Mobile Medical APPs in China," IJERPH, MDPI, vol. 17(8), pages 1-28, April.
    3. Jingfang Liu & Jun Kong & Xin Zhang, 2020. "Study on Differences between Patients with Physiological and Psychological Diseases in Online Health Communities: Topic Analysis and Sentiment Analysis," IJERPH, MDPI, vol. 17(5), pages 1-17, February.
    4. Ailian Zhang & Mengmeng Pan, 2020. "“Smart Process” of Medical Innovation: The Synergism Based on Network and Physical Space," IJERPH, MDPI, vol. 17(11), pages 1-17, May.
    5. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    6. Yating Zhao & Changyong Liang & Zuozuo Gu & Yunjun Zheng & Qilin Wu, 2020. "A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot," IJERPH, MDPI, vol. 17(8), pages 1-19, April.
    7. Yuanyuan Cao & Jiantong Zhang & Liang Ma & Xinghong Qin & Junjun Li, 2020. "Examining User’s Initial Trust Building in Mobile Online Health Community Adopting," IJERPH, MDPI, vol. 17(11), pages 1-20, June.

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