Hybrid data mining approaches for prevention of drug dispensing errors

Lien Chin Chen, Chun Hao Chen, Hsiao Ming Chen, S. Tseng

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)305-327
Number of pages23
JournalJournal of Intelligent Information Systems
Volume36
Issue number3
DOIs
StatePublished - 1 Jun 2011

Keywords

  • Classification modeling
  • Decision tree
  • Dispensing errors
  • Logistic regression
  • Medical risk management

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