基于ARIMA模型结合回归分析在产科工作量预测中的应用
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摘要
目的:探讨ARIMA模型结合回归方程在产科工作量预测中的应用价值。方法:以产科门诊建卡数与分娩量数据为基础,运用SPSS19.0建立月建卡数的ARIMA模型、月建卡数与月分娩量的回归方程模型,采用实际数据验证模型,评价模型,选择精度较高的模型进行2018-2020年的产科工作量预测。结果:月建卡数的模型ARIMA(1,1,1)(0,1,1)_(12)的拟合效果最优,用实际分娩量进行预测精度验证,预测分娩量与实际分娩量基本吻合,且实际值均在预测值可信区间范围内。符合拟合程度较高以及线性回归的显著性检验要求的回归方程为y=313.727+1.212x,其预测的平均误差为5.114%,具有较高的精确度,并对历史值的预测效果较好。采用ARIMA模型结合线性回归分析预测2018-2020年建卡人数分别为11 324、12 388、13 334,增长率依次为4.62%、9.40%、7.64%;分娩量预测值分别为12 936、13 554、14 369,增长率依次为9.61%、4.78%、6.01%。结论:ARIMA模型结合回归分析具有较高的预测精度,可较好地拟合产科工作量的演变趋势,为新生育政策下产科管理提供决策依据。
Objective:To explore application of ARIMA model combined with regression analysis in prediction of obstetric workload.Method:Based on the number of health care cards in obstetric outpatient and delivery volume,the ARIMA model of the number of health care cards per month,the regression equation model of the number of health care cards per month and the delivery volume per month were established by SPSS 19.0.The actual data were used to validate the model,and the evaluation model was selected to predict the obstetric workload from 2018 to 2020. Result:The model ARIMA(1,1,1)(0,1,1)_(12) has the best fitting effect.The prediction accuracy was verified by the actual delivery volume.The predicted delivery volume basically coincides with the actual delivery volume,and the actual value was within the confidence interval of the predicted value.The regression equation which accords with the higher fitting degree and the significance test of linear regression is y=313.727+1.212 x.The average error of prediction was 5.114%.It had higher accuracy and better prediction effect for historical values.ARIMA model combined with linear regression analysis was used to predict the number of health care cards from 2018 to 2020 were respectively 11 324,12 388,13 334,the growth rate were respectively 4.62%,9.40% and 7.64%;the predicted value of delivery volume were respectively 12 936,13 554,14 369,the growth rate were respectively 9.61%,4.78% and 6.01%.Conclusion:ARIMA model combined with regression analysis has a high prediction accuracy,can better fit the evolution trend of obstetric workload,provide decision-making basis for obstetric management under the new birth policy.
Objective:To explore application of ARIMA model combined with regression analysis in prediction of obstetric workload.Method:Based on the number of health care cards in obstetric outpatient and delivery volume,the ARIMA model of the number of health care cards per month,the regression equation model of the number of health care cards per month and the delivery volume per month were established by SPSS 19.0.The actual data were used to validate the model,and the evaluation model was selected to predict the obstetric workload from 2018 to 2020. Result:The model ARIMA(1,1,1)(0,1,1)_(12) has the best fitting effect.The prediction accuracy was verified by the actual delivery volume.The predicted delivery volume basically coincides with the actual delivery volume,and the actual value was within the confidence interval of the predicted value.The regression equation which accords with the higher fitting degree and the significance test of linear regression is y=313.727+1.212 x.The average error of prediction was 5.114%.It had higher accuracy and better prediction effect for historical values.ARIMA model combined with linear regression analysis was used to predict the number of health care cards from 2018 to 2020 were respectively 11 324,12 388,13 334,the growth rate were respectively 4.62%,9.40% and 7.64%;the predicted value of delivery volume were respectively 12 936,13 554,14 369,the growth rate were respectively 9.61%,4.78% and 6.01%.Conclusion:ARIMA model combined with regression analysis has a high prediction accuracy,can better fit the evolution trend of obstetric workload,provide decision-making basis for obstetric management under the new birth policy.
引文
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[17]林海锋,陶红兵,程兆辉,等.武汉地区医院产科资源利用效率及影响因素研究[J].中国医疗管理科学,2015,5(5):9-13.
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[19]陈银苹,吴爱萍,余亮科,等.组合模型对肺结核发病趋势的预测研究[J].中国全科医学,2014,17(21):2452-2456.
[20]范引光,吕金伟,戴色莺,等.ARIMA模型与灰色预测模型GM(1,1)在HIV感染人数预测中的应用[J].中华疾病控制杂志,2012,16(12):1100-1103.
[2]朱玮,仇静波,黄群.ARIMA模型在分娩量预测中的应用[J].中国妇幼保健,2015,30(20):3345-3347.
[3]莫佳琪,刘志辉,江岚,杨滨波,朱淳.基于ARIMA模型的产房分娩情况预测研究[J].中国妇幼保健,2014,29(16):2469-2472.
[4]中华人民共和国国家卫生健康委员会.《中华人民共和国母婴保健法实施办法》(中华人民共和国国务院第308号令发布)[EB/OL].[2001-6-20]http://www.nhfpc.gov.cn/zwgk/fagui/201304/8db10e2a43e843e58f69289ee4c172cb.shtml.
[5]邱林.应用ARIMA模型预测某三级甲等医院门诊量[J].中国医院统计,2018,25(1):77-79.
[6]龚磊,吴家兵,侯赛.ARIMA模型在安徽省流行性感冒发病预测中的应用[J].公共卫生与预防医学,2015,26(2):4-7.
[7] Mai Q,Aboagye‐Sarfo P,Sanfilippo F M,et al.Predicting the number of emergency department presentations in Western Australia:A population-based time series analysis[J].Emergency Medicine Australasia,2015,27(1):16-21.
[8]周惠,陈晓军,张杨,等.ARIMA模型在肺结核登记病例数预测中的应用[J].江苏预防医学,2018,29(03):286-288.
[9]樊雯婧,陆群,邹立巍,等.ARIMA模型在合肥市疟疾发病预测中的应用[J].安徽医科大学学报,2013,48(3):252-256.
[10]陈莉.探讨ARIMA模型在细菌性痢疾发病预测中的应用[J].中国卫生统计,2011,28(4):417-419.
[11]顾刘金.应用SPSS软件实现多重线性回归分析[J].预防医学,2018,30(3):323-324.
[12]金丕焕,陈峰.医用统计方法[M].3版.上海:复旦大学出版社,2009.
[13]赵志,周倩,张晋昕.时间序列分析方法及其进展[J].中国卫生统计,2015,32(6):1087-1090.
[14]陈伟,陈正利,李少芳,等.ARIMA模型在河南省梅毒月发病率预测中的应用[J].中国卫生统计,2013,30(4):604-606.
[15]胡良平.多重线性回归分析的核心内容与关键技术概述[J].四川精神卫生,2018,31(1):1-6.
[16]杨婷,王芳,宋莉,等.“全面二孩”政策下产科床位需求与缺口测算[J].中国卫生政策研究,2016,9(2):59-64.
[17]林海锋,陶红兵,程兆辉,等.武汉地区医院产科资源利用效率及影响因素研究[J].中国医疗管理科学,2015,5(5):9-13.
[18]赵薇,黄爱群,胡焕青,等.我国大中城市助产机构产科床位配置及使用现状研究[J].中国妇幼保健,2015,30(12):1811-1813.
[19]陈银苹,吴爱萍,余亮科,等.组合模型对肺结核发病趋势的预测研究[J].中国全科医学,2014,17(21):2452-2456.
[20]范引光,吕金伟,戴色莺,等.ARIMA模型与灰色预测模型GM(1,1)在HIV感染人数预测中的应用[J].中华疾病控制杂志,2012,16(12):1100-1103.