Hybrid data mining approaches for prevention of drug dispensing errors

doi:10.1007/s10844-009-0107-6

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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:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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