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Data Mining Analysis of Relationship Between Blood Stream Infection and Clinical Background in Patients Undergoing Lactobacillus Therapy

机译:乳酸乳杆菌治疗患者血流感染与临床背景关系的数据挖掘分析

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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 were 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, 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. 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.
机译:本研究的目的是通过使用数据挖掘来分析乳酸杆菌治疗的影响和背景危险因素对患者血流感染的影响。通过数据挖掘软件,即“图标矿工”(Koden Industry Co.,Ltd。)分析数据。从细菌检测与血液样本和患者的治疗之间的决定树中提取了重要的“if-DON规则”,例如乳酸杆菌治疗,抗生素,各种导管等。施加Chi-Square测试,差异比和物流回归为了分析乳杆菌治疗对细菌检测的影响。从乳酸杆菌的缺乏率的差距,乳酸杆菌的细菌缺失的风险约为2(95%CI:1.57-2.99)。重要的“if-then规则”,Chi-Square试验,赔率比和逻辑回归显示,乳酸杆菌治疗可能是预防血流感染的重要因素。我们的研究表明,乳酸杆菌治疗可能有效降低血流感染的风险。数据挖掘对于从我们的临床数据库中提取血流感染的背景风险因素是有用的。

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