Prevention of Drug Dispensing Errors by Using Hybrid Data Mining Approaches

• Main Authors: Hsiao-ming Chen, 陳小明
• Other Authors: Shin-mu Tseng
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/61860258367373868836

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 96 === One important issue in medical care is the prevention of drug dispensing errors since they caused numerous injuries and deaths with expensive cost. In this thesis, we propose a hybrid data mining approach with an implemented system to solve this problem. Our approach consists of two main modules, HDMmodel and HDMclustering. In HDMmodel, J48 and logistic regression are used to derive the decision tree and regression function from the given dispensing error cases and drug database. In HDMclustering, similar drugs, which are easily confused with each other, are then gathered together into clusters by the clustering technique named PoCluster and the extracted logistic regression function. Risky drug pairs that may cause dispensing errors are then alerted in our implemented system with interpretable prevention rules. Finally, by the experimental evaluation on real datasets in a medical center, our approach is shown to be capable of diagnosing the potential dispensing errors effectively.