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ZHANG Chengzhong, WAN He, JIANG Yiping, JIA Dan, FENG Kunmiao, XIN Hailiang. Process optimization of Morinda officinalis with wine steaming by star dot design-response surface methodology[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(5): 447-450. doi: 10.12206/j.issn.1006-0111.202003188
Citation: FU Xiang, YANG Zhang-wei, CHEN Sheng-xin, CHEN Chang-hong, HE Yu-tao. Data mining of antibiotic prophylactic use for cesarean section patients[J]. Journal of Pharmaceutical Practice and Service, 2012, 30(2): 109-114. doi: 10.3969/j.issn.1006-0111.2012.02.009

Data mining of antibiotic prophylactic use for cesarean section patients

doi: 10.3969/j.issn.1006-0111.2012.02.009
  • Received Date: 2011-10-08
  • Rev Recd Date: 2011-12-29
  • Objective To establish, compare and evaluate the classification models of antibiotic prophylactic use for cesarean section patients for the targeted intervention in future. Method PASW® Modeler 13 was applied to establish classification models and to get the influential variables (clinical factors) in antibiotic prophylactic use. Results With the data of 787 cases, the classification models were established, in which, Bayesian networks, logistic regression and CHAID were better. In 21 clinical factors, blood loss was the most influential variable. Conclusion The data mining technique was able to quickly create models reflecting the use of prophylactic antibiotics use for cesarean section, which would provide a new analysis tool for drug use survey.
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    [3] 姜 涛.785例产妇抗菌药物应用的合理性分析[J].中国医院药学杂志,2010,30(10):884.
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Data mining of antibiotic prophylactic use for cesarean section patients

doi: 10.3969/j.issn.1006-0111.2012.02.009

Abstract: Objective To establish, compare and evaluate the classification models of antibiotic prophylactic use for cesarean section patients for the targeted intervention in future. Method PASW® Modeler 13 was applied to establish classification models and to get the influential variables (clinical factors) in antibiotic prophylactic use. Results With the data of 787 cases, the classification models were established, in which, Bayesian networks, logistic regression and CHAID were better. In 21 clinical factors, blood loss was the most influential variable. Conclusion The data mining technique was able to quickly create models reflecting the use of prophylactic antibiotics use for cesarean section, which would provide a new analysis tool for drug use survey.

ZHANG Chengzhong, WAN He, JIANG Yiping, JIA Dan, FENG Kunmiao, XIN Hailiang. Process optimization of Morinda officinalis with wine steaming by star dot design-response surface methodology[J]. Journal of Pharmaceutical Practice and Service, 2020, 38(5): 447-450. doi: 10.12206/j.issn.1006-0111.202003188
Citation: FU Xiang, YANG Zhang-wei, CHEN Sheng-xin, CHEN Chang-hong, HE Yu-tao. Data mining of antibiotic prophylactic use for cesarean section patients[J]. Journal of Pharmaceutical Practice and Service, 2012, 30(2): 109-114. doi: 10.3969/j.issn.1006-0111.2012.02.009
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