运用时间序列模型预测门诊患者抗菌药物使用率趋势
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篇名: 运用时间序列模型预测门诊患者抗菌药物使用率趋势
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摘要: 目的:加强抗菌药物门诊应用管理,促进抗菌药物的合理使用,为医院的科学管理决策提供参考。方法:统计我院2008年1月-2016年6月的门诊患者使用抗菌药物例次占同期门诊总例次比例,将2008-2015年的门诊患者抗菌药物使用率数据用于建立自回归移动平均模型(ARIMA),2016年上半年数据用于验证所建立的模型,并预测2016年下半年门诊患者抗菌药物使用率趋势;采用SPSS 20.0软件进行统计分析。结果:建立的ARIMA(2,1,0)(2,1,0)12模型具有较高的拟合度, 2016年上半年门诊患者抗菌药物使用率实际值与拟合值相差很小,平均绝对误差为0.72%,平均相对误差为4.20%,且都在拟合值的95%置信区间内;模型预测值的动态趋势与实际值基本一致。结论:ARIMA较好地模拟了医院门诊患者抗菌药物使用率趋势,可用于门诊患者抗菌药物使用率趋势的短期预测和动态分析,但在远期预测时,还应综合多方面因素考虑。
ABSTRACT: OBJECTIVE: To strengthen application management of antibiotics in outpatients, promote rational use of antibiotics, and to provide reference for scientific management and decision-making in the hospital. METHODS: The proportion of outpatients receiving antibiotics in total outpatients was analyzed statistically during Jan. 2008-Jun. 2016. Utilization rate data of antibiotics in outpatients during 2008-2015 were used to establish Autoregressive integrated moving average model(ARIMA), and the data of the first half of 2016 was used to validate established model; the utilization rate trend of antibiotics in outpatients in the second half of 2016 was predicted. SPSS 20.0 statistical software was adopted for statistical analysis. RESULTS: Established ARIMA (2,1,0) (2,1,0) 12  model has higher fitting degree. There was a small difference between measured value and fitted value of utilization rate of antibiotics in outpatients in 2016. Average absolute error was 0.72%, and average relative error was 4.20%, within 95% confidence interval of fitted value. Dynamic trend of model predicted value was basically consistent with measured value. CONCLUSIONS: ARIMA model simulates utilization rate trend of antibiotics in outpatients well, can be used for short-term prediction and dynamic analysis of utilization rate trend of antibiotics. However, for long-term prediction, various factors should be considered.
期刊: 2017年第28卷第23期
作者: 柳海环,柳海琛,吴晨帆,郑芳芳
AUTHORS: LIU Haihuan,LIU Haichen,WU Chenfan,ZHENG Fangfang
关键字: 抗菌药物;时间序列;自回归移动平均模型;预测
KEYWORDS: Antibiotics; Time series; Autoregressive integrated moving average model; Prediction
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