Abstract
Forecasting the average Length of Stay (LoS) of a patient is prime aspect for all hospitals to effectively determine and plan services demanded at various level. Prediction of LoS plays a vital role in strategic decision making by health care administrators. In this paper, a dynamic computational model based on time series, implemented using soft computing techniques is presented to forecast average length of stay of patient. Aim of designing proposed model is to overcome the drawbacks of the exiting approaches and derive more robust and accurate methodology to forecast LoS of patient. Subsequently, the performance of the proposed model is demonstrated by comparing the results of proposed model with some of the pre-existing forecasting methods. In general, the findings of the study are interesting and superior in terms of least Average Forecasting Error Rate (AFER) and Mean Square Error (MSE) values.
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References
Chassin, M.R.: Length of stay and outcome: myocardial infarction, variations in hospital length of stay: their relationship to health outcomes. Health Technologies Case Study 4, NTIS Order #PB84-111483 (August 1983)
Weissman, C.: Analyzing intensive care unit length of stay data: problems and possible solutions. Critical Care Medicine 25(9), 1594–1599 (1997)
Clarke, A., Rosen, R.: Length of stay, how short should hospital care be? European Journal of Public Health 11(2), 166–170 (2001)
Walczak, S., Scorpio, R., Pofahl, W.: Predicting hospital length of stay with neural networks. In: The Proceedings of the Eleventh International FLAIRS Conference, pp. 333–337 (1998)
Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets and Systems 54, 269–277 (1993)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series Part I. Fuzzy Sets and Systems 54, 1–9
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series: Part II. Fuzzy Sets and Systems 62, 1–8 (1994)
Song, Q.: A note on fuzzy time series model selection with sample autocorrelation functions. Cybernetics and Systems: An International Journal 34, 93–107 (2003)
Chen, S.M., Hsu, C.-C.: A new method to forecasting enrollments using fuzzy time series. International Journal of Applied Science and Engineering 2(3), 234–244 (2004)
Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems 81, 311–319 (1996)
Chen, S.M.: Forecasting enrollments based on high-order fuzzy time series. Cybernetics and Systems: An International Journal 33, 1–16 (2002)
Huarng, K.: Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems 123, 369–386 (2002)
Huarng, K.: Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems 12, 387–394 (2001)
Hsu, C.C., Chen, S.M.: A new method for forecasting enrollments based on fuzzy time series. In: The Proceedings of Seventh Conference on Artificial Intelligence and Applications, Taichung, Taiwan, Republic of China, pp. 17–22
Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems 100, 217–228 (1998)
Lee, L.W., Wang, L.W., Chen, S.M.: Handling forecasting problems based on two-factors high-order time series. IEEE Transactions on Fuzzy Systems 14(3), 468–477 (2006)
Li, H., Kozma, R.: A dynamic neural network method for time series prediction using the KIII model. In: The Proceedings of the 2003 International Joint Conference on Neural Networks, vol. 1, pp. 347–352 (2003)
Melike, S., Degtiarev, K.Y.: Forecasting Enrollment Model Based on First-Order Fuzzy Time Series. In: The Proceedings of World Academy of Science, Engineering and Technology, vol. 1, pp. 1307–6884 (2005)
Melike, S., Konstsntin, Y.D.: Forecasting enrollment model based on first-order fuzzy time series. In: The Proceedings of the International Conference on Computational Intelligence, Istanbul, Turkey (2004)
Jilani, T.A., Burney, S.M.A., Ardil, C.: Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning. Proceedings of World Academy of Science, Engineering and Technology 23, 333–338 (2007)
Jilani, T.A., Burney, S.M.A., Ardil, C.: Multivariate high order fuzzy time series forecasting for car road accidents. International Journal of Computational Intelligence 4(1), 15–20 (2007)
Singh, S.R.: A computational method of forecasting based on fuzzy time series. International Journal of Mathematics and Computers in Simulation 79, 539–554 (2008)
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Garg, B., Sufyan Beg, M.M., Ansari, A.Q., Imran, B.M. (2011). Soft Computing Model to Predict Average Length of Stay of Patient. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_24
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DOI: https://doi.org/10.1007/978-3-642-19423-8_24
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