Study on prediction of clinical demand for plasma components in Suzhou city based on ARIMA model
10.13303/j.cjbt.issn.1004-549x.2021.12.024
- VernacularTitle:基于ARIMA模型的苏州市区血浆类成分血临床需求预测研究
- Author:
Shuhong XIE
1
;
Sijing ZHANG
1
;
Mingyuan WANG
1
;
Qi XIAO
1
;
Yan YU
1
;
Weibing YAN
1
Author Information
1. Suzhou Blood Center, Suzhou 215006, China
- Publication Type:Journal Article
- Keywords:
ARIMA model;
plasma;
clinical demand;
prediction
- From:
Chinese Journal of Blood Transfusion
2021;34(12):1370-1373
- CountryChina
- Language:Chinese
-
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.
