Archives
Current issue
About
About us
Aims and Scope
Abstracting and Indexing
Editorial Office
Open Access Policy
Publication Ethics
Journal History
Publisher
Follow us: Twitter
Contact
For Authors
Editorial Policy
Peer Review Policy
Manuscript Preparation Guidelines
Copyright & Licencing
Publication Fees
Fee Waiver Policy for Doctoral Students
EJMSTE Language Editing Service
More
Statistics
Special Issues
Special Issue Announcements
Published Special Issues
SPECIAL ISSUE PAPER
Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model
1
School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, CHINA
2
Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, CHINA
3
Department of Management Sciences, City University of Hong Kong, Kowloon, HONG KONG
Online publication date: 2017-11-14
Publication date: 2017-11-14
EURASIA J. Math., Sci Tech. Ed 2017;13(12):7615-7627
KEYWORDS
patient visits forecastingtime series datadecomposition and ensemblewavelet decompositionartificial neural networks
ABSTRACT
Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of “decomposition and ensemble”. In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.
REFERENCES (50)
1.
Abdel-Aal, R. E., & Mangoud, A. M. (1998). Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis. Computer Methods & Programs in Biomedicine, 56(3), 235–247.
2.
Abraham, G., Byrnes, G. B., & Bain, C. (2009). Short-term forecasting of emergency inpatient flow. Information Technology in Biomedicine, IEEE Transactions on, 13(3), 380-388.
3.
Aladag, C. H., & Aladag, S. (2012). Forecasting the number of outpatient visits with different activation functions. Advances in Time Series Forecasting, 26.
4.
Au-Yeung, S. W., Harder, U., Mccoy, E. J., Knottenbelt, W. J. (2009). Predicting patient arrivals to an accident and emergency department. Emergency Medicine Journal, 26(4), 241-244.
5.
Batal, H., Tench, J., Mcmillan, S., Adams, J., & Mehler, P. S. (2001). Predicting patient visits to an urgent care clinic using calendar variables. Academic Emergency Medicine Official Journal of the Society for Academic Emergency Medicine, 8(1), 48–53.
6.
Benhmad, F. (2011). A wavelet analysis of oil price volatility dynamic. Economics Bulletin, 31(1), 792-806.
7.
Bergs, J., Heerinckx, P., & Verelst, S. (2014). Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. International Emergency Nursing, 22(2), 112–115.
8.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2011). Time series analysis: forecasting and control. John Wiley & Sons.
9.
Boyle, J., Wallis, M., Jessup, M., Crilly, J., Lind, J., Miller, P., & Fitzgerald, G. (2008). Regression forecasting of patient admission data. Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. IEEE, 3819-3822.
10.
Brillman, J. C., Burr, T., Forslund, D., Joyce, E., Picard, R., & Umland, E. (2005). Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance. BMC Medical Informatics & Decision Making, 5:4(1), 1-14.
11.
Chen, C. F., Ho, W. H., Chou, H. Y., Yang, S. M., Chen, I. T., & Shi, H. Y. (2011). Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model. Computational & Mathematical Methods in Medicine, 2011(3), 713-750.
12.
Cheng, C. H., Wang, J. W., & Li, C. H. (2008). Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix. Expert Systems with Applications, 34(4), 2568-2575.
13.
Dan, T, & Qualls, C. (1994). Time series forecasts of emergency department patient volume, length of stay, and acuity. Annals of Emergency Medicine, 23(2), 299–306.
14.
Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia: Society for industrial and applied mathematics.
15.
Diaz, J., Alberdi, J. C., & Pajares, M. S. (2001). A model for forecasting emergency hospital admissions: effect of environmental variables. Journal of Environmental Health, 64(3), 9-15.
16.
Flottemesch, T. J., Gordon, B. D., Jones, S. S. (2007). Advanced Statistics: Developing a formal model of emergency department census and defining operational efficiency. Academic Emergency Medicine Official Journal of the Society for Academic Emergency Medicine, 14(9), 799–809.
17.
Friede, K. A., Osborne, M. C., Erickson, D. J., Roesler, J. S., Azam, A., Croston, J. K., McGonigal, M. D., & Ney, A. L. (2009). Predicting trauma admissions: the effect of weather, weekday, and other variables. Minnesota medicine, 92(11), 47-49.
18.
Garg, B., Beg, M. M. S., & Ansari, A. Q. (2012). A new computational fuzzy time series model to forecast number of outpatient visits. Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American. IEEE, 1-6.
19.
Geva, A. B., Kerem, D. H. (1998). Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering. Biomedical Engineering, IEEE Transactions on, 45(10), 1205-1216.
20.
Hadavandi, E., Shavandi, H., Ghanbari, A., Abbasian-Naghneh, S. (2012). Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Applied Soft Computing, 12(2), 700-711.
21.
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feed-forward networks are universal approximators. Neural Networks, 2(89), 359–366.
22.
Jones, S. S. (2009). A multivariate time series approach to modeling and forecasting demand in the emergency department. Journal of Biomedical Informatics, 42(1), 123-139.
23.
Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008). Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine, 15(2), 159–170.
24.
Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38(9), 490-491.
25.
Kam, H. J., Sung, J. O., & Park, R. W. (2010). Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthcare Informatics Research, 16(3), 158-165.
26.
Kim, K., Lee, C., O’Leary, K., Rosenauer, S., & Mehrotra, S. (2014). Predicting Patient Volumes in Hospital Medicine: A Comparative Study of Different Time Series Forecasting Methods. Tech. rep., Northwestern University.
27.
Koestler, D. C., Ombao, H., & Bender, J. (2013). Ensemble-based methods for forecasting census in hospital units. BMC Medical Research Methodology, 13(6), 454-462.
28.
Kovats, R. S., Hajat, S., & Wilkinson, P. (2004). Contrasting patterns of mortality and hospital admissions during hot weather and heat waves in Greater London, UK. Occupational & Environmental Medicine, 61(11), 893–898.
29.
Littig, S. J., & Isken, M. W. (2007). Short term hospital occupancy prediction. Health Care Management Science, 10(1), 47-66.
30.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 11(7), 674-693.
31.
Marcilio, I., Hajat, S., & Gouveia, N. (2013). Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Academic Emergency Medicine, 20(8), 769-777.
32.
Milner, P. C. (1997). Ten-year follow-up of ARIMA forecasts of attendances at accident and emergency departments in the Trent region. Statistics in Medicine, 16(18), 2117–2125.
33.
Morzuch, B. J., & Allen, P. G. (2006). Forecasting hospital emergency department arrivals. 26th Annual Symposium on Forecasting, Santander, Spain.
34.
Pan, W. T. (2017). A Newer Equal Part Linear Regression Model: A Case Study of the Influence of Educational Input on Gross National Income. Eurasia Journal of Mathematics, Science & Technology Education, 13(8), 5765-5773.
35.
Reis, B. Y., & Mandl, K. D. (2003). Time series modeling for syndromic surveillance. BMC Medical Informatics & Decision Making, 3(1), 1-11.
36.
Rotstein, Z., Wilf-Miron, R., Lavi, B., Shahar, A., Gabbay, U, & Noy, S. (1997). The dynamics of patient visits to a public hospital ED: a statistical model. American Journal of Emergency Medicine, 15(6), 596-599.
37.
Safar, A., & Alkhezzi, F. (2016). Students’ perspectives of the impact of online streaming media on teaching and learning at the college of education at Kuwait University. Eurasia Journal of Mathematics, Science & Technology Education, 12(12).
38.
Schweigler, L. M., Desmond, J. S., McCarthy, M. L., Bukowski, K. J., Ionides, E. L., & Younger, J. G. (2009). Forecasting models of emergency department crowding. Academic Emergency Medicine, 16(4), 301-308.
39.
Sierra, G., Reinhardt, L., Fetsch, T., Martinez-Rubio, A., Makijarvi, M, Yli-Mayry, S., Montonen, J., Katila, T., Borggrefe, M., & Breithardt, G. (). Risk stratification of patients after myocardial Infarction based on wavelet decomposition of the signal-averaged Electrocardiogram. Annals of Noninvasive Electrocardiology, 2(1), 47-58.
40.
Sun, Y., Heng, B. H., Seow, Y. T., et al. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emergency Medicine, 9(1), 1.
41.
Tang, L., Dai, W., Yu, L., & Wang, S. (). A novel CEEMD-based EELM ensemble learning paradigm for crude oil price forecasting. International Journal of Information Technology & Decision Making, 14(01), 141-169.
42.
Tang, L., Yu, L., Wang, S., Li, J., & Wang, S. (). A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Applied Energy, 93, 432-443.
43.
Wang, S., Tang, L., & Yu, L. (2011). SD-LSSVR-based decomposition-and-ensemble methodology with application to hydropower consumption forecasting. Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization. IEEE Computer Society, 603-607.
44.
Wang, S., Yu, L., Tang, L., & Wang, S. (2011). A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Fuel & Energy Abstracts, 36(11), 6542–6554.
45.
White, H. (1990). Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings. Neural Networks, 3(90), 535-549.
46.
Xu, M., Wong, T. C., & Chin, K. S. (2013). Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network. Decision Support Systems, 54(3), 1488-1498.
47.
Yu, L., Dai, W., Tang, L. (2015). A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Engineering Applications of Artificial Intelligence. http://dx.doi.org/10.1016/j.en....
48.
Yu, L., Wang, S., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623-2635.
49.
Yu, L., Zhao, Y., & Tang, L. (2014). A compressed sensing based AI learning paradigm for crude oil price forecasting. Energy Economics, 46(C), 236–245.
50.
Zibners, L. M. (2006). Local weather effects on emergency department visits: a time series and regression analysis. Pediatric Emergency Care, 22(2), 104-106.
Share
RELATED ARTICLE
| eISSN: | 1305-8223 |
| ISSN: | 1305-8215 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
