Analysis and prevention of dispension errors by using data mining techniques

Vincent Shin-Mu Tseng, Chun Hao Chen, Hsiao Ming Chen, Hui Jen Chang, Chin Tai Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Medical treatment techniques have been improved continuously in the past years. However, the better approaches are still needed to solve medical treatment problems. One important topic in this field is the analysis and prevention of medication errors. In this paper, we focus on the problem of dispensing error that is one important problem of medication errors and we proposed a prevention model by using three approaches. The proposed dispensing error mining framework consists of two phases, namely the modeling and prediction phases. Firstly, Statistical approach (logistic regression) and data mining approaches (C4.5 and SVM) are used to analyze dispensing error problem and to build classification models. Three kinds of factors, namely drug-names factor, drug-properties factor and environmental factor, with totally thirteen attributes are used in the modeling phase. In prediction phase, new drugs thus can be analyzed for the probability of dispensing error by the model so as to prevent dispensing error. At last, experimental results on real dataset showed that the proposed approach is effective and the considered factors can actually increase the accuracy of the model.

Original languageEnglish
Title of host publication2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007
Pages65-70
Number of pages6
StatePublished - 1 Dec 2007
Event2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Publication series

Name2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007

Conference

Conference2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007
CountryUnited States
CityHonolulu, HI
Period1/04/075/04/07

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