Data Mining Analysis of Relationship between Blood Stream Infection and Clinical Background in Patients Undergoing Lactobacillus Therapy
In this paper, we applied data mining for extracting certain patterns from our hospital clinical microbiology database. The aim of this study is to analyze the effects of lactobacillus therapy and the background risk factors on blood stream infection in patients by using data mining.The data was analyzed by data mining software, i.e. ICONS Miner (Koden Industry 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, anti-biotics, 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.Our study suggests that lactobacillus therapy may be effective in reducing the risk of blood stream infection. Data mining is useful for extracting background risk factors of blood stream infection from our clinical database.
Kimiko Matsuoka Shigeki Yokoyama Kunitomo Watanabe Shusaku Tsumoto
Osaka Prefectural General Medical Center, Osaka 558-8558, Japan Koden Industry Co., Ltd, Tokyo 143-0015, Japan Gifu University, Gifu 501-1194, Japan Shimane University, Izumo 693-8501, Japan
国际会议
北京
英文
1971-1976
2007-05-23（万方平台首次上网日期，不代表论文的发表时间）