[1]王 慧,宋雪霏,杨辰玲,等.先天性上睑下垂住院患者特征分析及人数预测[J].医学信息,2023,36(13):14-18，23.[doi:10.3969/j.issn.1006-1959.2023.13.003]
　WANG Hui,SONG Xue-fei,YANG Chen-ling,et al.Characteristics Analysis and Number Prediction of Hospitalized Patients with Congenital Ptosis[J].Journal of Medical Information,2023,36(13):14-18，23.[doi:10.3969/j.issn.1006-1959.2023.13.003]
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先天性上睑下垂住院患者特征分析及人数预测()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年13期
页码:
14-18，23
栏目:
临床信息学
出版日期:
2023-07-01

文章信息/Info

Title:
Characteristics Analysis and Number Prediction of Hospitalized Patients with Congenital Ptosis
文章编号:
1006-1959（2023）13-0014-06
作者:
王 慧; 宋雪霏; 杨辰玲; 等
（上海交通大学医学院附属第九人民医院眼科/上海市眼眶病眼肿瘤重点实验室，上海 200011）
Author(s):
WANG Hui; SONG Xue-fei; YANG Chen-ling; et al.
(Department of Ophthalmology,Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine/Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology,Shanghai 200011,China)
关键词:
ARIMA乘积季节模型; 住院量; 病案统计; 先天性上睑下垂
Keywords:
ARIMA multiplicative seasonal model; The amount of hospitalization; Medical record statistics; Congenital ptosis
分类号:
R777.1
DOI:
10.3969/j.issn.1006-1959.2023.13.003
文献标志码:
A
摘要:
目的 通过对住院病案首页数据进行分析，了解先天性上睑下垂住院患者基本情况，探索自回归综合移动平均（ARIMA）乘积季节模型预测住院人数的适用性，为科室合理安排医务人员和医疗资源提供依据。方法 以2013年1月-2019年12月我院眼科先天性上睑下垂患者病案首页数据为基础，描述性分析患者基本情况，绘制患者地区分布条形图，建立住院人数的ARIMA乘积季节模型，计算平均绝对百分误差（MAPE）和平均相对误差（MRE），检验模型的预测能力。结果 2013-2019年共收治先天性上睑下垂患者3646例，患者平均年龄（6.68±5.28）岁，源自国内外30个地区、15个民族，平均住院天数（3.91±1.63）d，平均住院总费用（1.06±0.39）万元。通过R语言auto.arima语句，自动确定最优模型为ARIMA（1，0，1）×（0，1，1）12，AIC=478.94，Ljung-Box Q=4.738，P=0.578，并且该模型的所有参数均通过统计学检验（P＜0.001）；2013年1月-2019年6月拟合值与实际值之间的MAPE为11.92，2019年7月-12月预测值与实际值之间的MRE为19.95%。结论 ARIMA乘积季节模型可基本拟合先天性上睑下垂住院人数的演变趋势，今后可应用于科室住院人数、门诊人数、医疗器械使用量等的预测。
Abstract:
Objective To understand the basic situation of hospitalized patients with congenital ptosis by analyzing the data on the medical record home page, and to explore the applicability of the product seasonal model of auto-regressive integrated moving averages (ARIMA) to predict the number of hospitalized patients, so as to provide a basis for the reasonable arrangement of medical staff and medical resources.Methods Based on the data of the medical record home page of patients with congenital ptosis in ophthalmology department of our hospital from January 2013 to December 2019, the basic situation of patients was descriptively analyzed, the bar chart of regional distribution of patients was drawn, and the ARIMA product seasonal model of the number of inpatients was established. The mean absolute percentage error (MAPE) and mean relative error (MRE) were calculated to test the predictive ability of the model.Results From 2013 to 2019, 3646 patients with congenital ptosis were treated, with an average age of (6.68±5.28) years, from 30 regions and 15 nationalities at home and abroad. The average length of hospitalization was (3.91±1.63) days, and the average total hospitalization cost was (1.06±0.39) ten thousand yuan. The optimal model was automatically determined as ARIMA(1,0,1)×(0,1,1)12 through R language auto.arima statement, AIC=478.94, Ljung-Box Q=4.738，P=0.578. And all the parameters of the model passed the statistical test (P<0.001). The MAPE between the fitted value and the actual value from January 2013 to June 2019 was 11.92, and the MRE between the predicted value and the actual value from July 2019 to December 2019 was 19.95%.Conclusion The ARIMA multiplicative seasonal model can basically fit the evolution trend of congenital ptosis hospitalization number, and can be applied to the prediction of department hospitalization number, outpatient number, medical device use, etc.

参考文献/References:

[1]郑锴,杨美娟,郝峰.全面质量管理在综合医院住院诊疗管理中的运用探讨[J].中国医院管理,2015,35(11):41-42.[2]吕兰婷,林夏,傅金澜,等.现代医院管理制度下医疗质量及效率研究:基于某三甲医院术前平均住院日分析[J].中国医院,2019,23(8):25-27.[3]覃桓.基于多重回归模型的住院人数预测[J].中国卫生统计,2014,31(3):523-524.[4]王亮生,林丹华,曾一清.临床路径管理对医院医疗管理质量及患者平均住院时间的影响[J].卫生职业教育,2017,35(23):135-137.[5]郭晴,田庆丰,朱春燕,等.ARIMA模型与GM(1,1)模型下我国公立医院人均住院费用和药占比的预测分析[J].医学与社会,2021,34(1):14-19.[6]孔令伟,牛希华,徐林刚.不同术式治疗先天性上睑下垂患者的疗效比较[J].中国医疗美容,2021,11(1):19-22.[7]周瑾,李梦,赵伟,等.单侧先天性上睑下垂患儿眼球生物学参数及屈光状态的研究[J].中国斜视与小儿眼科杂志,2021,29(2):5-7,15.[8]张魏魏,任晓霞,谢娟,等.重度先天性上睑下垂伴弱视患者的脉络膜厚度分析[J].中华实验眼科杂志,2020,38(9):788-794.[9]Barnett AG,Stephen D,Huang C,et al.Time series models of environmental exposures:Good predictions or good understanding[J].Environ Res,2017,154:222-225.[10]Chaurasia V,Pal S.COVID-19 Pandemic: ARIMA and Regression Model-Based Worldwide Death Cases Predictions[J].SN Comput Sci,2020,1(5):288.[11]Ramezanian M,Haghdoost AA,Mehrolhassani MH,et al.Forecasting health expenditures in Iran using the ARIMA model (2016-2020)[J].Med J Islam Repub Iran,2019,33:25.[12]Polwiang S.The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)[J].BMC Infect Dis,2020,20(1):208.[13]唐路,宋萍,谢冰珏,等.基于R语言的ARIMA乘积季节模型对重庆某儿童医院门诊量的预测分析[J].医学信息,2021,34(11):19-22.[14]顾梅,王雪梅,施海彬.基于ARIMA乘积季节模型的某医院介入导管室手术量预测研究[J].中国现代手术学杂志,2020,24(3):231-236.[15]田德红,丁国武,于国伟,等.自回归求和移动平均乘积季节模型在我国布鲁菌病短期月发病人数预测中的应用[J].中国全科医学,2015,18(33):4100-4104.[16]胡烨.幼儿重度先天性上睑下垂的手术治疗[D].杭州:浙江大学,2016.[17]杨海霞.0～6岁小儿眼病筛查总结与分析及筛查策略探讨[D].济南:山东大学,2018.[18]尚小平,刘福荣,魏乐,等.郑州市某三甲医院住院患者地域来源分布及影响因素分析[J].郑州大学学报(医学版),2019,54(1):131-134.[19]李乐乐,杜天天.人口年龄变化、医疗需求行为与医疗费用增长:基于医疗保险结算数据的实证研究[J].当代经济管理,2021,43(4):72-80.[20]金乐.先天性上睑下垂自体阔筋膜悬吊术后二次修复的回顾性分析[D].杭州:浙江大学,2016.[21]李梦.单侧先天性上睑下垂儿童双眼视觉功能与生物学参数及屈光状态相关分析[D].广州:广州医科大学,2017.[22]Pourahmadi M,Noorbaloochi S.Multivariate time series analysis of neuroscience data: some challenges and opportunities[J].Curr Opin Neurobiol,2016,37:12-15.[23]Li Z,Li Y.A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS[J].BMC Med Inform Decis Mak,2020,20(1):143.[24]Molina LL,Angón E,García A,et al.Time series analysis of bovine venereal diseases in La Pampa, Argentina[J].PLoS One,2018,13(8):e0201739.[25]Kokolakakis T,Lera-Lopez F.Sport Promotion through Sport Mega-Events.An Analysis for Types of Olympic Sports in London 2012[J].Int J Environ Res Public Health,2020,17(17):6193.[26]曲红梅,白亚娜,魁发瑞,等.组合模型对恶性肿瘤死亡率拟合度评价及预测方法的研究[J].中华流行病学杂志,2017,38(1):117-120.[27]Akhtar M,Kraemer MUG,Gardner LM.A dynamic neural network model for predicting risk of Zika in real time[J].BMC Med,2019,17(1):171.

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更新日期/Last Update: 1900-01-01