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目的分析邯郸地区ABO血型系统各血型临床红细胞类用血分布规律,依据时间序列分析方法建立预测数学模型,并进行预测,指导血液机构的相关业务工作。方法对邯郸市2002-2013年每月各血型向临床供应红细胞类制品的量及合计血量进行建模。行Epidata3.0双录入数据,导入IBM SPSS Statistics 21,利用时间序列模型中专家建模器对各血型临床用血量及总血量建立数学模型,并预测2014年1-6月份的临床血液需求量,验证模型误差。结果专家建模器对红细胞类供血量给出的模型除了AB型为ARIMA(2,1,0)(0,1,1)以外,其余血型及总血量模型均为ARIMA(0,1,1)(0,1,1)模型。对5个模型残差的白噪声检验结果均显示P>0.05,说明残差均为白噪声序列,模型均提取了原序列中所有数据信息,模型诊断均得以通过。将预测结果与实际值进行比较,实际值均落入预测值的95%可信区间内,且平均相对误差较小,所得模型均为最优模型。结论通过建立数学模型的方式,补充后续数据,血液机构能够合理指导相应的业务工作,科学合理满足临床用血,规划用血趋势。
OBJECTIVE: To analyze the distribution of blood erythrocytes in blood of ABO blood group system in Handan area, establish the prediction mathematical model based on time series analysis, and make prediction to guide the related business of blood agency. Methods The amount of blood erythrocytes and the total amount of blood supplied to clinical clinics from 2002 to 2013 in Handan City were modeled. Line Epidata3.0 double-entry data into the IBM SPSS Statistics 21, the use of time-series model expert modeler for each blood type of clinical blood and total blood volume to establish a mathematical model and predict the clinical blood demand from January to June 2014 Quantity, verify model error. Results The results of the model of expert erythrocyte blood supply were given in addition to AB type ARIMA (2,1,0) (0,1,1), the remaining blood type and total blood model are ARIMA (0,1 , 1) (0,1,1) model. The white noise test results of the 5 models showed P> 0.05, indicating that the residuals are all white noise sequences. All the models extracted the data of the original sequence, and the model diagnosis was passed. The predicted results are compared with the actual values, the actual values fall within the 95% confidence interval of the predicted value, and the average relative error is small, the resulting model is the best model. Conclusion By establishing the mathematical model and supplementing the follow-up data, the blood agency can reasonably guide the corresponding business work and scientifically and reasonably meet the clinical blood use and planning the blood use trend.