基于季节ARIMA模型对某三级综合性医院门诊量的预测研究
Prediction of outpatient visits in tertiary general hospitals based on seasonal ARIMA model
陈文娟 1林建潮1
作者信息
- 1. 绍兴第二医院医共体总院,312000浙江绍兴
- 折叠
摘要
目的 通过建立季节ARIMA模型,对浙江省某三级综合性医院门诊量进行预测,为医院合理配备门诊人力资源提供依据.方法 以2013年1-6月浙江省某医院门诊量数据为基线,利用SPSS软件构建季节ARIMA模型,对2023年7-12月的门诊量进行预测,通过对比门诊量实测值,评价季节ARIMA模型预测门诊人次的精度.结果 该综合性医院门诊量呈现逐年上升趋势,并呈现周期性波动的特征.拟合的最优季节ARIMA模型为ARIMA(0,1,1)(1,0,1)12,BIC(贝叶斯信息准则)为5.273,MAPE(平均绝对百分误差)为14.265,R2(模块决定系数)为0.408,总体相对误差为1.83％,预测结果良好.结论 季节ARIMA模型较好地模拟了该三级综合性医院门诊量在时间序列上的变化趋势,为该院门诊量的短期预测提供理论依据.
Abstract
Objective To predict the outpatient volume of a tertiary general hospital in Zhejiang Province by establishing the seasonal ARIMA model,and to provide a basis for the rational allocation of outpatient human resources.Methods Based on the outpatient visits data of a tertiary general hospital in Zhejiang Province from January 2013 to June 2023,the seasonal ARIMA model was constructed by SPSS software to predict the annual outpatient visits from July 2023 to December 2023.By comparing the measured outpatient visits,the accuracy of the seasonal ARIMA model was evaluated.Results The outpatient volume of the general hospital showed an increasing trend year by year,and showed the characteristics of periodic fluctuations.The optimal sea-sonal ARIMA model fitted was ARIMA(0,1,1)(1,0,1)12,BIC(Bayesian information criterion)was5.273,MAPE(mean ab-solute percentage error)was 14.265,R2(module determination coefficient)was 0.408,and the overall relative error was 1.83％,indicating good prediction results.Conclusion The seasonal ARIMA model can simulate the change trend of the outpa-tient volume in the time series of the tertiary general hospital well,and provide a theoretical basis for the short-term forecast of the outpatient volume in the hospital.
关键词
季节ARIMA/门诊人次/时间序列分析/预测模型Key words
seasonal autoregressive integrated moving average model(ARIMA)/outpatient number/time series analy-sis/prediction model引用本文复制引用
基金项目
2023年柯桥区社发类经费自筹科技计划项目(2023KZ08)
出版年
2024
NETL
NSTL
万方数据