ARIMA与LSTM模型在医院出院人次预测中的比较研究

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...

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

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.