ARIMA与LSTM模型在医院出院人次预测中的比较研究  

A comparative study of ARIMA and LSTM models in predicting hospital discharge number

作　　者：王淑平 李敏[2] 杜敏[1] 刘杉 梁颖 罗建伟[1] WANG Shuping;LI Min;DU Min;LIU Shan;LIANG Ying;LUO Jianwei(Information Department,Hubei Cancer Hospital,Wuhan 430079,China;School of Computer Science,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北省肿瘤医院信息统计科,武汉430079 [2]湖北工业大学计算机学院

出　　处：《公共卫生与预防医学》2021年第1期18-21,共4页Journal of Public Health and Preventive Medicine

摘　　要：目的通过ARIMA乘积季节模型和LSTM神经网络模型拟合某三甲专科医院的月出院人次并进行预测,比较两种模型的预测效果。方法运用某三甲专科医院2013—2018年度的月出院人次,分别构建ARIMA乘积季节模型和LSTM神经网络模型,然后利用所得的模型对2019年度的月出院人次进行预测并与实际数据进行比较。采用平均绝对百分误差(MAPE)对模型的预测效果进行评价。结果ARIMA乘积季节模型和LSTM神经网络模型的预测数据与2019年度1~12月份实际出院人次的MAPE值分别为7.90%和14.26%。结论ARIMA乘积季节模型的预测效果要好于LSTM神经网络模型,ARIMA模型预测结果表明2019年度某三甲专科医院的月出院人次呈增长趋势,与实际数据的吻合度较好。Objective To fit and predict the monthly discharge number of a specialist hospital using Autoregressive Integrated Moving Average model(ARIMA)and Long Short-Term Memory Neural Network model(LSTM),and compare the prediction effects of the two models.Methods ARIMA and LSTM models were constructed based on the monthly discharge number of a specialist hospital from 2013 to 2018.The resulting models were then used to predict the monthly discharge numbers in 2019,which were compared with actual data.The mean absolute percentage error(MAPE)was used to evaluate the prediction effect of these two models.Results The MAPE values of ARIMA and LSTM compared to actual data in 2019 were 7.90%and 14.26%,respectively.Conclusion The prediction effect of ARIMA was better than that of LSTM.The prediction results of ARIMA showed that the number of patients discharged from the specialist hospital in 2019 was increasing,which fit well with the actual data.

关 键 词：ARIMA乘积季节模型 LSTM神经网络模型 出院人次 

分 类 号：R197[医药卫生—卫生事业管理]