Duration Prediction of Teleconsultation Services Based on the ATT-FC-LSTM Model
ZHAI Yunkai，JIA Qishuo，QIAO Yan，ZHAO Jie
1. Zhengzhou University, Zhengzhou, China; 2. National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou, China; 3. Henan International Joint Laboratory of Intelligent Health Information System, Zhengzhou, China; 4. The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Abstract：To address the characteristics of teleconsultation service duration, such as heterogeneity, dynamism, and randomness, a Long Short-Term Memory (LSTM) neural network algorithm combined with attention mechanisms with fully connected layers is used to predict the duration of teleconsultation services. The performance of the algorithm’s predictions is evaluated using both regression and classification methods. The model stacked an attention layer, three fully connected layers, and two LSTM layers to selectively focus on input data and achieve higher-level feature representation, thereby enhancing the model’s performance and representation capacity. Compared to four popular machine learning algorithms, the designed model performed better across multiple prediction performance evaluation metrics in classification predictions. Based on this, the four variables with the greatest impact on the duration of teleconsultation services were calculated, in order of significance: consulting expert, consulting department, whether the expert was late, and the expert’s title.
翟运开，贾启硕，乔岩，赵杰. 基于ATT-FC-LSTM模型的远程会诊服务时长预测[J]. 管理学报, 2025, 22(3): 568-.
ZHAI Yunkai，JIA Qishuo，QIAO Yan，ZHAO Jie. Duration Prediction of Teleconsultation Services Based on the ATT-FC-LSTM Model. Chinese Journal of Management, 2025, 22(3): 568-.
