Effective hospital outpatient forecasting is an important prerequisite for modern hospitals to implement intelligent management of medical resources. As outpatient visits flow may be complex and diverse volatility, we propose a hybrid Autoregressive Integrated Moving Average (ARIMA)-Long Short Term Memory (LSTM) model, which hybridizes the ARIMA model and LSTM model to obtain the linear tendency and nonlinear tendency correspondingly. Instead of the traditional methods that artificially assume the linear components and nonlinear components should be linearly added, we propose employing backpropagation neural networks (BP) to imitate the real relationship between them. The proposed hybrid model is applied to real data analysis and experimental analysis to justify its performance against single ARIMA model, single LSTM model and the hybrid ARIMA-LSTM model based on the traditional method. Compared with competitors, the proposed hybrid model produced the lowest RMSE, MAE and MAPE. It achieves more accurate and stable prediction. Therefore, the proposed model can be a promising alternative in outpatient visit predictive problems.

有效的医院门诊预测是现代医院实施医疗资源智能管理的重要前提。由于门诊流量可能复杂且波动多样，我们提出了一种混合的自回归综合移动平均（ARIMA）-长短期记忆（LSTM）模型，该模型将ARIMA模型和LSTM模型进行混合以获得相应的线性趋势和非线性趋势。代替人为地假定应该线性添加线性成分和非线性成分的传统方法，我们建议采用反向传播神经网络（BP）来模拟它们之间的真实关系。所提出的混合模型应用于实际数据分析和实验分析，以证明其针对单个ARIMA模型的性能是合理的，基于传统方法的单个LSTM模型和ARIMA-LSTM混合模型。与竞争对手相比，拟议的混合模型产生最低的RMSE，MAE和MAPE。它可以实现更准确和稳定的预测。因此，提出的模型可以作为门诊就诊预测问题的有希望的替代方法。