基于PSO的LSTM神经网络门诊量预测研究

Study on Outpatient Quantity Prediction of Long Short-term Memory Neural Network Based on Particle Swarm Optimization

研究基于粒子群优化算法的长短期记忆神经网络模型构建方法及其在医院门诊管理中的应用,选取三甲医院中医慢病相关科室门诊量历史记录数据,构建基于长短期记忆神经网络的门诊量预测模型,借助粒子群优化算法对长短期记忆网络进行参数优化,并使用优化后的模型对门诊量进行预测。月度门诊量预测结果表明,经过粒子群优化的长短期记忆神经网络模型在测试集上的预测误差RMSE,相比未优化的模型减小了48.5%。粒子群优化算法能高效地优化预测模型,可使模型较好地预测出门诊量变化趋势,从而为医务人员门诊管理工作提供决策支持。

To investigate the construction method of long short-term memory(LSTM)neural network model based on particle swarm optimization(PSO)algorithm and its application in hospital outpatient management,the historical outpatient quantity data of the department related to Chinese medicine chronic disease in the tertiary hospital are selected to establish an outpatient quantity prediction model by means of the long short-term memory neural network,and particle swarm optimization algorithm is used to optimize the parameters of the prediction model.The prediction results of monthly outpatient quantity show that the prediction error RMSE of the LSTM neural network model optimized by PSO on the test set is 48.5%lower than that of the unoptimized model.Particle swarm optimization algorithm can optimize the prediction model efficiently,so that the model can better predict the trends of outpatient quantity and provide decision support for the outpatient management of medical staff.