摘要： 目的 基于时间序列分析探讨悬浮红细胞临床需求预测模型,为血液资源采集和储备提供科学依据。方法 对重庆市万州中心血站2006年1月至2016年6月每月悬浮红细胞ABO各血型用量及总用量建立差分整合移动平均自回归(ARIMA)模型,运用最优模型预测2016年7月至12月每月悬浮红细胞ABO血型用量及总用量,验证预测效果。结果 各最优模型均通过残差序列自相关函数、偏自相关函数以及Ljung-Box Q检验,且各模型的预测值与同期悬浮红细胞用量的实际值变化趋势吻合度较高,平均相对误差较小,预测精度较高。结论 最优模型均能较好地拟合悬浮红细胞临床用量在时间序列上的变化趋势,可用于悬浮红细胞临床用量预测。

基于时间序列分析的悬浮红细胞临床需求预测模型研究

彭荣荣¹, 刘芸男¹, 杨冬燕², 王含柔¹, 赵明烽¹, 杨小丽¹

1. 重庆医科大学公共卫生与管理学院, 医学与社会发展研究中心, 健康领域社会风险预测治理协同创新中心, 重庆 400016;
2. 重庆市血液中心, 重庆 400015

收稿日期:2019-01-08出版日期:2020-06-30发布日期:2020-06-15
通讯作者:杨小丽E-mail:872463319@qq.com
作者简介:彭荣荣(1996-),女,硕士研究生.
基金资助:重庆市决策咨询与管理创新计划（cstc2016jccxBX0064）


关键词: 时间序列分析, 悬浮红细胞, 预测模型
Abstract: Objective To explore the clinical demand prediction model of suspended red blood cells using a time series analysis,and to provide a scientific basis for the collection and storage of blood resources. Methods Auto regressive integrated moving average (ARIMA) models were established to predict the ABO blood type usage and the total usage of suspended red blood cells that would be required monthly at Wanzhou Central Blood Station,Chongqing,China. These models were based on the actual usage required between January 2006 and June 2016. The models were used to predict the ABO blood type usage and the total usage of suspended red blood cells monthly from July to December 2016 to verify the prediction effect of the models. Results All the optimal models passed the autocorrelation function,the partial autocorrelation function of the residual sequence and the Ljung-Box Q test. The dynamic trends of the predicted values were generally consistent with the actual clinical usage of suspended red blood cells in the same period,with a small mean relative error and high prediction accuracy. Conclusion Optimal models better fit the clinical usage trend of suspended red blood cells in a time series. The ARIMA models can be used to predict the clinical usage of suspended red blood cells.
Key words: time series analysis, suspended red blood cells, prediction model
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