ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Hospital outpatient visit analysis and forecasting using time series models

  • 1.

    School of Computer and Informatien Engineering, Xinmen Unversity of Technology, Xiamen 361024, China

  • 2.

    The Wan Yanan Institute for Studies in Economics, Xiamen 361005, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.10.001
  • Received Date: 27 August 2015
  • Accepted Date: 29 September 2015
  • Rev Recd Date: 29 September 2015
  • Publish Date: 30 October 2015
    Abstract

  • Abstract

    Analysis and forecasting of hospital outpatient visits are important in making correct and feasible decisions for hospital resources management and high quality patient care provision. However, research in outpatient visit analysis and forecasting has not drawn much attentions so far, and current research mainly focuses on the computational methods for forecasting only, lacking in comprehensive analysis, rules finding, and knowledge discovery for hospital outpatient visits. Thus it was propsed to construct autoregressive moving average models (ARMAX), neural network models, and hybrid models integrating ARMAX and NN for outpatient visit analysis and forecasting. By constructing these models, the rules of the daily outpatient visit of the Xiamen city, China were analyzed comprehensively. It was fund that outpatient visit data show a significantly upward time trend, a significant day-of-week effect, and a significant serial autocorrelation. By comparing the forecasting performance of these time series models, it was fund that the ARMAX+NN hybrid model achieves better performance, which is mainly due to the fact that the hybrid model can capture both linear and nonlinear parts of the outpatient visit data.

    Abstract

    Analysis and forecasting of hospital outpatient visits are important in making correct and feasible decisions for hospital resources management and high quality patient care provision. However, research in outpatient visit analysis and forecasting has not drawn much attentions so far, and current research mainly focuses on the computational methods for forecasting only, lacking in comprehensive analysis, rules finding, and knowledge discovery for hospital outpatient visits. Thus it was propsed to construct autoregressive moving average models (ARMAX), neural network models, and hybrid models integrating ARMAX and NN for outpatient visit analysis and forecasting. By constructing these models, the rules of the daily outpatient visit of the Xiamen city, China were analyzed comprehensively. It was fund that outpatient visit data show a significantly upward time trend, a significant day-of-week effect, and a significant serial autocorrelation. By comparing the forecasting performance of these time series models, it was fund that the ARMAX+NN hybrid model achieves better performance, which is mainly due to the fact that the hybrid model can capture both linear and nonlinear parts of the outpatient visit data.
    • FullText(HTML)
  • loading
    • References(20)
  • [1]
    Cheng C H, Wang J W, Li C H. Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix[J]. Expert Systems with Applications, 2008, 34(4): 2568-2575.
    [2]
    Hadavandia E, Shavandi H, Ghanbaric A, et al. Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals[J]. Applied Soft Computing, 2012, 12(2): 700-711.
    [3]
    Garg B, Beg M S, Ansari A. A new computational fuzzy time series model to forecast number of outpatient visits[C]// 2012 Annual Meeting of the North American. Berkeley, USA: Fuzzy Information Processing Society, 2012: 1-6.
    [4]
    李婧, 陈瑛瑛, 霍永胜,等. 新疆某三级综合医院门诊量预测模型构建及应用[J]. 中国医院统计, 2015, 22(3): 183-185, 189.
    Li J, Chen Y Y, Huo Y S, et al. Construction and application of an ARIMA model for predicting the number of outpatient visits in a Xinjiang tertiary general hospital[J]. Chinese Journal of Hospital Statistics, 2015, 22(3): 183-185, 189.
    [5]
    周忠彬, 吕红梅, 邹郢. ARIMA干预模型在医院门诊量预测中的应用[J]. 中国医院统计, 2008, 15(2): 110-112.
    Zhou Z B, Lu H M, Zou Y. Time series analysis by ARIMA interfering model to forecast amount of outpatient[J]. Chinese Journal of Hospital Statistics, 2008, 15(2): 110-112.
    [6]
    Box G E P, Jenkins G M. Reinsel G C. Time Series Analysis: Forecasting and Control[M]. 3ed Englewood Cliffs, USA: Prentice-Hall,1994.
    [7]
    Yu L, Lai K K, Wang S Y. Multistage RBF neural network ensemble learning for exchange rates forecasting[J]. Neurocomputing, 2008, 71(16-18): 3295-3302.
    [8]
    Niu D X, Liu D, Wu D S. A soft computing system for day-ahead electricity price forecasting. Applied Soft Computing, 2010, 10(3): 868-875.
    [9]
    Chang J R, Wei L Y, Cheng C H. A hybrid ANFIS model based on AR and volatility for TAIEX forecasting[J]. Applied Soft Computing, 2011, 11(1): 1388-1395.
    [10]
    Xiao Y, Xiao J, Liu J, et al. A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting[J]. Journal of Systems Science and Complexity, 2014, 27(1): 225-236.
    [11]
    Rumelhart D, Hinton G, Williams R. Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533-536.
    [12]
    Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50(1): 159-175.
    [13]
    Huang W, Lai K, Nakamori Y, et al. Neural networks in finance and economics forecasting[J].International Journal of Information Technology & Decision Making, 2007, 6(1): 113-140.
    [14]
    Hornik K, Stinnchcombe M, White H. Multilayer feed forward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359-366.
    [15]
    Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting: A review and evaluation[J]. IEEE Transactions on Power Systems, 2001, 16(1): 44-55.
    [16]
    Xie J X, Cheng C T, Chau K W, et al. A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity[J]. International Journal of Environment and Pollution, 2006, 28(3/4): 364-381.
    [17]
    Yu L, Wang S Y, Lai K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computers & Operations Research, 2005, 32(10): 2523-2541.
    [18]
    Zhang G Q, Patuwo B E, Hu M Y. Forecasting with artificial neural networks: The state of the art[J]. International Journal of Forecasting, 1998, 14(1): 35-62.
    [19]
    Dickey D A, Fuller W A. Distribution of the estimators for autoregressive time series with a unit root[J]. Journal of the American Statistical Association, 1979, 74(366): 427-431.
    [20]
    Phillips P C B, Perron P. Testing for a unit rootin time series regression[J]. Biomètrika, 1986, 75(2): 335-346. )
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    Get Citation
    PDF
    XML
    [1]
    Cheng C H, Wang J W, Li C H. Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix[J]. Expert Systems with Applications, 2008, 34(4): 2568-2575.
    [2]
    Hadavandia E, Shavandi H, Ghanbaric A, et al. Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals[J]. Applied Soft Computing, 2012, 12(2): 700-711.
    [3]
    Garg B, Beg M S, Ansari A. A new computational fuzzy time series model to forecast number of outpatient visits[C]// 2012 Annual Meeting of the North American. Berkeley, USA: Fuzzy Information Processing Society, 2012: 1-6.
    [4]
    李婧, 陈瑛瑛, 霍永胜,等. 新疆某三级综合医院门诊量预测模型构建及应用[J]. 中国医院统计, 2015, 22(3): 183-185, 189.
    Li J, Chen Y Y, Huo Y S, et al. Construction and application of an ARIMA model for predicting the number of outpatient visits in a Xinjiang tertiary general hospital[J]. Chinese Journal of Hospital Statistics, 2015, 22(3): 183-185, 189.
    [5]
    周忠彬, 吕红梅, 邹郢. ARIMA干预模型在医院门诊量预测中的应用[J]. 中国医院统计, 2008, 15(2): 110-112.
    Zhou Z B, Lu H M, Zou Y. Time series analysis by ARIMA interfering model to forecast amount of outpatient[J]. Chinese Journal of Hospital Statistics, 2008, 15(2): 110-112.
    [6]
    Box G E P, Jenkins G M. Reinsel G C. Time Series Analysis: Forecasting and Control[M]. 3ed Englewood Cliffs, USA: Prentice-Hall,1994.
    [7]
    Yu L, Lai K K, Wang S Y. Multistage RBF neural network ensemble learning for exchange rates forecasting[J]. Neurocomputing, 2008, 71(16-18): 3295-3302.
    [8]
    Niu D X, Liu D, Wu D S. A soft computing system for day-ahead electricity price forecasting. Applied Soft Computing, 2010, 10(3): 868-875.
    [9]
    Chang J R, Wei L Y, Cheng C H. A hybrid ANFIS model based on AR and volatility for TAIEX forecasting[J]. Applied Soft Computing, 2011, 11(1): 1388-1395.
    [10]
    Xiao Y, Xiao J, Liu J, et al. A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting[J]. Journal of Systems Science and Complexity, 2014, 27(1): 225-236.
    [11]
    Rumelhart D, Hinton G, Williams R. Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533-536.
    [12]
    Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50(1): 159-175.
    [13]
    Huang W, Lai K, Nakamori Y, et al. Neural networks in finance and economics forecasting[J].International Journal of Information Technology & Decision Making, 2007, 6(1): 113-140.
    [14]
    Hornik K, Stinnchcombe M, White H. Multilayer feed forward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359-366.
    [15]
    Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting: A review and evaluation[J]. IEEE Transactions on Power Systems, 2001, 16(1): 44-55.
    [16]
    Xie J X, Cheng C T, Chau K W, et al. A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity[J]. International Journal of Environment and Pollution, 2006, 28(3/4): 364-381.
    [17]
    Yu L, Wang S Y, Lai K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computers & Operations Research, 2005, 32(10): 2523-2541.
    [18]
    Zhang G Q, Patuwo B E, Hu M Y. Forecasting with artificial neural networks: The state of the art[J]. International Journal of Forecasting, 1998, 14(1): 35-62.
    [19]
    Dickey D A, Fuller W A. Distribution of the estimators for autoregressive time series with a unit root[J]. Journal of the American Statistical Association, 1979, 74(366): 427-431.
    [20]
    Phillips P C B, Perron P. Testing for a unit rootin time series regression[J]. Biomètrika, 1986, 75(2): 335-346. )
    Recommended articles
    TrendMD
    Volume 45 Issue 10 page: 795-803
    Cover

    Article Metrics

    Article views (22) PDF downloads(87)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return