基于Prophet-LSTM-PSO组合模型的医院住院量预测研究

Prediction of hospital inpatients based on combined Prophet-LSTM-PSO model

针对医院住院量预测问题,首先利用先知模型(Prophet)与长短期记忆循环神经网络(LSTM)预测方法对2015年1月到2019年12月上海市东方医院呼吸内科住院量的时序数据进行建模分析,然后利用粒子群算法(PSO)求出两种模型对应的组合系数,从而得到最终的Prophet-LSTMPSO组合模型,并通过RMSE和MAE统计学指标将组合模型与单一模型进行对比,同时利用公开数据集进行对比实验。结果表明,Prophet-LSTM-PSO组合模型较Prophet、LSTM、移动平均自回归模型等(ARIMA)等单一模型有效地降低了医院住院量预测的偏离性,提高了预测精度。

Based on the prediction of hospital inpatients,first Prophet model and long-and short-term memory(LSTM)recurrent neural network prediction method were used to design a combined prediction model based on particle swarm optimization(PSO).Both Prophet and LSTM neural network models were used to simulate and analyze the time series data of the inpatients of respiratory medicine in Shanghai Oriental Hospital from January 2015 to December 2019.Then the particle swarm algorithm was used to find the corresponding combination coefficients of the two models to obtain the final Prophet-LSTM-PSO combined model.By the RMSE and MAE statistical indicators,the combined model was compared with the single model.At the same time,comparative experiments were carried out with open datasets.Results show that Prophet-LSTM-PSO combined model can effectively reduce prediction deviation and improve prediction accuracy compared with single models including Prophet,LSTM,and autoregressive integrated moving average model(ARIMA).