Incremental Integration of Probabilistic Models Learned from Data

The aim of this study is to analyze the effects of lactobacillus therapy and the background risk factors on blood stream infection in patients from our hospital clinical microbiology database by data mining. The data was analyzed by data mining software, i.e. "ICONSMiner" (KodenIndustry Co., Ltd.). The significant "if-then rules" were extracted from the decision tree between bacteria detection on blood samples and patients\´ treatments, such as lactobacillus therapy, antibiotics, various catheters, etc. The chi-square test, odds ratio and logistic regression were applied in order to analyze the effect of lactobacillus therapy to bacteria detection. From odds ratio of lactobacillus absence to lactobacillus presence, bacteria detection risk of lactobacillus absence was about 2 (95%CI: 1.57-2.99). The significant "If-then rules", chi-square test, odds ratio and logistic regression showed that lactobacillus therapy might be the significant factor for prevention of blood stream infection. Data mining is useful for extracting background risk factors of blood stream infection from our clinical database.