摘要
目的比较和评价不同时间序列模型预测医院感染发病率的效果,探索可用于预测医院感染发病率的最佳模型。方法以上海某三级甲等医院2011—2016年累计72个月的月度医院感染发病率数据作为拟合集构建季节性自回归移动平均模型(ARIMA)、NAR神经网络模型和ARIMA-BPNN组合模型,以2017年1—12月的月度感染发病率数据作为预测集检验模型的预测效果,评价比较不同模型的预测效果。结果对于拟合集,ARI-MA模型、NAR神经网络模型和ARIMA-BPNN组合模型的MAPE分别为13.00%、14.61%和11.95%;对预测集,三者的MAPE分别为15.42%、26.31%和14.87%。结论三种时间序列模型对医院感染发病率均具有较好的预测效果,其中ARIMA-BPNN组合模型对拟合和预测该院医院感染发病情况最佳,可为医院决策提供一定的数据支持。
Objective To compare and evaluate the effect of different time series models in predicting incidence of healthcare-associated infection(HAI),and explore the best model for predicting incidence of HAI.Methods Seasonal autoregressive integrated moving average(ARIMA)model,nonlinear autoregressive neural network(NARNN),and ARIMA-back propagation neural network(ARIMA-BPNN)combination model were constructed based on fitting dataset of monthly HAI incidence from 2011 to 2016(72 months)in a tertiary first-class hospital in Shanghai,predicting dataset of monthly infection incidence from January to December 2017 were used to test the predictive effect of model,the predictive effect of different models was evaluated and compared.Results For the fitting dataset,mean absolute percentage error(MAPE)of ARIMA,NARNN,and ARIMA-BPNN combination model were 13.00%,14.61%,and 11.95%respectively;and for the predicting dataset,MAPE of ARIMA,NARNN,and ARIMA-BPNN combination model were 15.42%,26.31%,and 14.87%respectively.Conclusion Three time series models can effectively predict the incidence of HAI,of which the ARIMA-BPNN combination model showed the best performance in fitting and predicting the occurrence of HAI in this hospital,and can provide data support for the hospital decision-making.
作者
陈越火
顾翔宇
于志臻
CHEN Yue-huo;GU Xiang-yu;YU Zhi-zhen(Department of Healthcare-associated Infection Management, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China)
出处
《中国感染控制杂志》
CAS
CSCD
北大核心
2019年第2期147-152,共6页
Chinese Journal of Infection Control
关键词
医院感染
ARIMA
ARIMA-BPNN组合模型
NAR神经网络
预测
healthcare-associated infection
autoregressive integrated moving average
ARIMA
ARIMA-BPNN combination model
nonlinear autoregressive neural network
NARNN
prediction