基于LSTM和注意力机制的远程会诊需求预测

Forecasting Teleconsultation Demand Based on LSTM and Attention Mechanism

为更准确地预测远程会诊需求量,提高远程会诊资源配置效率,文中引入多元回归分析(Multiple Linear Regression)和注意力机制来优化长短期记忆网络(LSTM)。首先,根据远程会诊需求中存在的假期效应生成假期指标,通过多元回归分析选取显著性高的指标作为模型输入,然后根据长短期记忆网络学习输入指标的内部复杂映射关系,利用注意力机制对指标分配不同权重,最后根据权重和LSTM隐藏层输入预测结果。基于国家远程医疗中心(NTCC)的实际历史会诊数据,研究MLR-Attention-LSTM的预测性能,并比较其与整合移动平均自回归模型、支持向量机、K近邻、BP神经网络和LSTM神经网络5种模型的预测效果。结果表明,优化后的LSTM模型预测精度最高。进一步地,探究假期指标对模型性能的影响,结果表明假期指标的输入可以进一步提高模型的预测精度,验证了MLR-Attention-LSTM和假期相关变量输入在远程会诊需求预测领域的可行性与适用性,为远程医学中心实际应用提供了理论支撑和实践指导。

To predict the demand for teleconsultation more accurately and improve the efficiency of resource allocation for teleconsultation,this paper introduces multiple linear regression and attention mechanism to optimize Long Short-term Memory network.Firstly,according to the holiday effect existing in the teleconsultation demand,the holiday index is generated,and the index with high significance is selected as the model input through multiple regression analysis.Then,according to the long-term short-term memory network to learn the internal complex mapping relationship of the input indicators,the attention mechanism is used to assign different weights to the indicators.Finally,the prediction results are input according to the weight and LSTM hidden layer.Based on the actual historical teleconsultation data of the National Telemedicine Center,this paper studies the predictive ability of MLR-Attention-LSTM,and compares it with the ARIMA,SVR,KNN,BP neural network and long short-term memory network.The results show that the improved LSTM model has the highest prediction accuracy.Furthermore,this paper explores the impact of holiday indicators on the performance of the model.The results show that the input of holiday indicators can further improve the prediction accuracy of the model.It verifies the feasibility and applicability of MLR-Attention-LSTM and holiday-related variable input in the field of teleconsultation demand prediction,and provides theoretical support and practical guidance for the practical application of telemedicine centers.