DocumentCode :
2489835
Title :
Comparison of different models to analyze the number of patients in waiting-list
Author :
Giraldo, B.F. ; Bolea, Y. ; Caminal, P.
Author_Institution :
Departament ESAII, Univ. Politecnica de Catalunya, Barcelona, Spain
Volume :
3
fYear :
2002
fDate :
23-26 Oct. 2002
Firstpage :
1877
Abstract :
In this paper we have studied the behavior of the hospital activity analyzing the number of patients in waiting-list. This work considers different methods to determine the best structure of the model. The parametric models that have been analyzed are ARX, ARMAX and OE. The prediction error method has been used to estimate the model parameters. The models have been compared according with different error criteria: V, FPE and MAE. The autocorrelation and cross-correlation tests have been applied to validate the models. The best compromise between low MAE and low sensitivity of FPE has been obtained with ARMAX models. Simulations of three different surgeries with a mean absolute error less than 9 patients have been obtained using ARMAX models with a reduced number of parameters.
Keywords :
autoregressive moving average processes; medical information systems; modelling; parameter estimation; prediction theory; surgery; ARMAX; ARX; OE; admission requests; autocorrelation tests; cross-correlation tests; hospital activity; hospital information system; identification; input-output linear model; mean absolute error; parametric models; patients in waiting-list; prediction error method; simulation; Autocorrelation; Hospitals; Information systems; Input variables; Parameter estimation; Parametric statistics; Predictive models; Signal processing; Surges; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
ISSN :
1094-687X
Print_ISBN :
0-7803-7612-9
Type :
conf
DOI :
10.1109/IEMBS.2002.1053072
Filename :
1053072
Link To Document :
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