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Comparative Study
. 2025 May 26;15(1):18287.
doi: 10.1038/s41598-025-03106-4.

Comparison of SES method and SARIMA model in predicting the number of admissions in the department of neurology

Wanjun Yang  1 , Liping Ding  1 , Aonan Su  2
Affiliations
Comparative Study

Comparison of SES method and SARIMA model in predicting the number of admissions in the department of neurology

Wanjun Yang et al. Sci Rep. .

Abstract

To establish and compare the prediction effect of SES and SARIMA model, and select the best prediction model to predict the number of patients in neurology department. The data came from HIS and medical record management system of a Grade-A hospital in Zhejiang Province. The number of inpatients from January 2019 to September 2023 was selected to establish SES and SARIMA model, respectively. Compare the fitting parameters, The larger the R2_adjusted, R2, the smaller the RMSE, MAPE, MAE and standardized BIC, The better model is selected. Finally, the established model was used to predict the number of hospital admissions from October to December 2023, and the prediction effect of the MRE judgment model was compared. The number of admissions to the department of neurology shows a cyclical change, and drops sharply in January-February each year and rises rapidly in March. The best fitting models of SES model and SARIMA model were Winters addition model and SARIMA(0,1,1)(0,1,1)12 model, respectively. The two models were selected to predict the number of admissions in the Department of neurology from October to December 2023, and the average relative error was 0.04 and 0.03, respectively. The prediction effect of SARIMA(0,1,1)(0,1,1)12 model was better. Age and Spring Festival may be the factors that affect the periodic change of the number of admissions in neurology department. Both SES and SARIMA model can be used to predict the number of admissions in the department of neurology, and the SARIMA model may be better.

Keywords: Department of neurology; Number of hospital admissions; Predict; SARIMA model; SES model.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: This study was approved by the Ethics Committee of Zhejiang Provincial People’s Hospital. All subjects gave written informed consent.

Figures

Fig. 1
Fig. 1
Sequence chart of admissions in the Department of Neurology from January 2019 to December 2023.
Fig. 2
Fig. 2
Residual diagram of Winters addition model.
Fig. 3
Fig. 3
Prediction of admissions in the Department of Neurology in October-December 2023 with Winters’ addition model.
Fig. 4
Fig. 4
Sequence diagram of the number of admissions in neurology department after first-order trend difference and first-order periodic difference.
Fig. 5
Fig. 5
ACF plot and PACF plot of the sequence after difference.
Fig. 6
Fig. 6
SARIMA (0,1,1) (0,1,1) 12 model residual ACF plot and PACF plot.
Fig. 7
Fig. 7
Q-Q plot of residual normality of SARIMA (0,1,1) (0,1,1) 12 model.
Fig. 8
Fig. 8
SARIMA (0,1,1) (0,1,1) 12 model predicted the number of admissions to the Department of Neurology in October-December 2023.
See this image and copyright information in PMC

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References

    1. Liyan Liu, Tinggui, L. I. et al. Construction of department comprehensive evaluation index system in tertiary hospitals. Chin. J. Health Qual. Control. 25 (4), 53–56 (2018).
    1. Zhang, S. et al. Using data mining technology to assist the establishment of hospital operation strategy objective. Chinese J. Med. Res. Administration, 2005(5):272–275 .
    1. Bao, Y. et al. Patient experience with outpatient encounters at public hospitals in Shanghai: examining different aspects of physician services and implications of overcrowding. PLoS One. 12 (2), e0171684 (2017). - PMC - PubMed
    1. MH, Y. et al. Overcrowding in emergency departments: a review of strategies to decrease future challenges. J. Res. Med. Sci.22 (1), 23 (2017). - PMC - PubMed
    1. Wu, H. Qimin Xiao. Analysis of the seasonal index of the number of inpatients in various specialized wards in a hospital. China Health Stat.39 (02), 225–227 (2022).

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