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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75773

    Title: Hybrid data mining approaches for prevention of drug dispensing errors
    Authors: Chen, Lien-Chin;Chen, Chun-Hao;Chen, Hsiao-Ming;Tseng, Vincent S.
    Contributors: 淡江大學資訊工程學系
    Keywords: Dispensing errors;Classification modeling;Decision tree;Logistic regression;Medical risk management
    Date: 2010-06
    Issue Date: 2012-04-13 18:11:51 (UTC+8)
    Publisher: New York: Springer New York LLC
    Abstract: Prevention of drug dispensing errors is an importance topic in medical care. In this paper, we propose a risk management approach, namely Hybrid Data Mining (HDM), to prevent the problem of drug dispensing errors. An intelligent drug dispensing errors prevention system based on the proposed approach is then implemented. The proposed approach consists of two main procedures: First, the classification modeling and logistic regression approaches are used to derive decision tree and regression function from the given dispensing errors cases and drug databases. In the second procedure, similar drugs are then gathered together into clusters by combing clustering technique (PoCluster) and the extracted logistic regression function. The drugs that may cause dispensing errors will then be alerted through the clustering results and the decision tree. Through experimental evaluation on real datasets in a medical center, the proposed approach was shown to be capable of discovering the potential dispensing errors effectively. Hence, the proposed approach and implemented system serve as very useful application of data mining techniques for risk management in healthcare fields.
    Relation: Journal of Intelligent Information Systems 36(3), pp.305-327
    DOI: 10.1007/s10844-009-0107-6
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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