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Decision supporting method for chronic disease patients based on mining frequent pattern tree

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Abstract

As the development of IT convergence technology reaches its zenith, data in almost all areas have been developed and operated as a system after digitalization. To acquire more diverse and in-depth information, humans are actively engaged in information filtering. In the medical and health industries, most medical information is organized in a system and utilized for efficient health management as well as in various areas such as U-healthcare. Due to aging and chronic disease, interest in health management has intensified. As a result, health prevention and management through U-healthcare has been developed. However, there has been no study on pain in patients suffering from chronic disease. Regarding pain-related decisions by patients, sustainable and effective management is required, unlike acute disease patients. In this paper, we proposes the decision supporting method for chronic disease patients based on mining frequent pattern tree. The proposed method is measures for pain-related decision making by chronic disease-suffering patients using a frequent pattern tree for data preprocessing, extraction, and data mining of conventional medical data. By utilizing the basic information of patients, which are the foundation for pain-related decision making, normalization can be applied to the frequent pattern tree of data mining. The pain forecast supports pain-related decision making by extracting similar patients’ information in a frequent pattern tree based on Electronic Medical Records (EMR).

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Notes

  1. Wonju Severance Christian Hospital, http://www.wch.or.kr/

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Acknowledgments

This work was supported by the Industrial Strategic technology development program, 10037283, funded By the Ministry of Trade, Industry & Energy (MI, Korea).

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Authors and Affiliations

  1. IS Lab., School of Computer Information Engineering, Sangji University, 63, Usan-dong, Wonju-si, Gangwon-do, 220-702, Korea

    Hoill Jung

  2. School of Computer Information Engineering, Sangji University, 63, Usan-dong, Wonju-si, Gangwon-do, 220-702, Korea

    Kyung-Yong Chung

  3. Department of Computer Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon, 406-799, Korea

    Young-Ho Lee

Authors
  1. Hoill Jung
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  2. Kyung-Yong Chung
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  3. Young-Ho Lee
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Correspondence to Young-Ho Lee.

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Jung, H., Chung, KY. & Lee, YH. Decision supporting method for chronic disease patients based on mining frequent pattern tree. Multimed Tools Appl 74, 8979–8991 (2015). https://doi.org/10.1007/s11042-013-1730-3

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