ABSTRACT
【Objective】 To establish an ARIMA model suitable for clinical platelet demand prediction in Suzhou, which can be used as reference to predict future clinical platelet demand and provide scientific basis for platelet collection, preparation, stock management and clinical deployment for blood banks, so as to achieve the maximum balance between platelets supply and demand . 【Methods】 The data of platelet consumption in Suzhou from 2009 to 2019 were collected and analyzed by SPSS 26 software, Time series analysis method was used to establish the ARIMA model. The model was further optimized through model identification, parameter estimation and optimal model test, and then used to predict clinical platelet consumption 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 for the prediction of platelet clinical demand was ARIMA (0, 1, 1) (0, 1, 1) 12. The ACF autocorrelation function value and PACF partial autocorrelation function value of the residuals were within 95% CI. Meanwhile, the LJUNG BOX test was 13.982 (P>0.05), indicating that there was no autocorrelation in the residuals. The trend of the curve between the predicted and actual value was basically the same(except for February 2020), and the predicted values were within 95% CI, with the average relative error of 7.22%, which was lower than 10%, showing good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction of clinical platelets demand in Suzhou, and can provide basis for reasonable collection, preparation and deployment of platelets.
ABSTRACT
【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.