Yang Wanjun, Ding Liping, Su Aonan
Medical Department, Zhejiang Provincial People's Hospital/People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Gongshu District, Hangzhou City, 310000, Zhejiang Province, China.
Sci Rep. 2025 May 26;15(1):18287. doi: 10.1038/s41598-025-03106-4.
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 R_adjusted, R, 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) 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) 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.
建立并比较简单指数平滑（SES）模型和自回归整合移动平均（SARIMA）模型的预测效果，选择最佳预测模型来预测神经内科的患者数量。数据来自浙江省一家三甲医院的医院信息系统（HIS）和病历管理系统。选取2019年1月至2023年9月的住院患者数量分别建立SES模型和SARIMA模型。比较拟合参数，调整后R方（R_adjusted）、R方（R）越大，均方根误差（RMSE）、平均绝对百分比误差（MAPE）、平均绝对误差（MAE）和标准化贝叶斯信息准则（standardized BIC）越小，则所选模型越好。最后，使用建立的模型预测2023年10月至12月的住院人数，并比较平均相对误差（MRE）判断模型的预测效果。神经内科的住院人数呈周期性变化，每年1-2月急剧下降，3月迅速上升。SES模型和SARIMA模型的最佳拟合模型分别为温特斯加法模型和SARIMA(0,1,1)(0,1,1)模型。选择这两个模型预测2023年10月至12月神经内科的住院人数，平均相对误差分别为0.04和0.03。SARIMA(0,1,1)(0,1,1)模型的预测效果更好。年龄和春节可能是影响神经内科住院人数周期性变化的因素。SES模型和SARIMA模型均可用于预测神经内科的住院人数，且SARIMA模型可能更好。
