基于ARIMA模型的苏州市区血浆类成分血临床需求预测研究

Study on prediction of clinical demand for plasma components in Suzhou city based on ARIMA model

目的采用时间序列分析方法中的ARIMA模型建立苏州市区临床血液需求预测模型,通过梳理临床用血的历史数据规律来预测未来的临床血液需求值,从而指导血液资源的合理采集与科学调配,实现临床血液的供需平衡。方法获取苏州市区2009~2019年每月血浆类成分血的临床使用情况数据,采用SPSS26软件进行数据分析及ARIMA模型构建,通过模型识别、参数估计及最优模型检验,确定临床血液预测的最优模型;运用所得最优模型对2020年1~11月的血浆类成分血临床用量进行预测,将预测值与实际数值进行对比,验证模型预测效果。结果最优模型为ARIMA(0,1,1)(0,1,1)_(12),残差的ACF自相关函数值和PACF偏自相关函数值均在95%CI内,同时杨-博克斯Q统计量值为11.596,P>0.05,通过白噪声检验。对2020年1~11月苏州市区血浆类成分血临床用量进行预测,预测值均在95%CI内,与实际值趋势一致,平均相对误差较小,为7.9%,模型预测效果较好。结论 ARIMA模型可用于短期预测苏州市区血浆类成分血临床用量,为合理采集、制备和科学调配提供依据。

Objective To establish a prediction model of clinical blood demand in Suzhou urban area by ARIMA model, and to predict future clinical blood demand by sorting out the historical data, so as to guide the reasonable collection and scientific deployment of blood resources, and achieve the balance of clinical blood supply and demand. Methods The monthly data of clinical use of plasma components in Suzhou city from 2009 to 2019 were obtained, and analyzed by SPSS26 software and ARIMA model. Through model identification, parameter estimation and optimal model test, the optimal model for clinical blood prediction was determined and used to predict the clinical consumption of plasma components from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model.Results The optimal model was ARIMA(0,1,1)(0,1,1)12. The values of ACF autocorrelation function and PACF partial autocorrelation function of residual were both within 95%CI. Meanwhile, the Yang-Box Q statistic value was 11.596, P>0.05, which passed the white noise test. The predicted values of clinical consumption of plasma components in Suzhou urban area from January to November 2020 were all within 95%CI, consistent with the trend of actual values, with small mean relative error(7.9%) and good prediction effect. Conclusion ARIMA model can be used for short-term prediction on clinical use of plasma components in Suzhou city, and provide reference for reasonable collection, preparation and scientific deployment.