Abstract: 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.

Key words: Long short-term memory, Attention mechanism, Teleconsultation, Demand forecasting, Holiday effect