IEEE J Biomed Health Inform. 2022 Feb;26(2):922-933. doi: 10.1109/JBHI.2021.3093897. Epub 2022 Feb 4.
Early prediction of influenza plays an important role in minimizing the damage caused, as it provides the resources and time needed to formulate preventive measures. Compared to traditional mechanistic approach, deep/machine learning-based models have demonstrated excellent forecasting performance by efficiently handling various data such as weather and internet data. However, due to the limited availability and reliability of such data, many forecasting models use only historical occurrence data and formulate the influenza forecasting as a multivariate time-series task. Recently, attention mechanisms have been exploited to deal with this issue by selecting valuable data in the input data and giving them high weights. Particularly, self-attention has shown its potential in various forecasting tasks by utilizing the predictive relationship between objects from the input data describing target objects. Hence, in this study, we propose a forecasting model based on self-attention for regional influenza forecasting, called SAIFlu-Net. The model exploits a long short-term memory network for extracting time-series patterns of each region and the self-attention mechanism to find the similarities between the occurrence patterns. To evaluate its performance, we conducted extensive experiments with existing forecasting models using weekly regional influenza datasets. The results show that the proposed model outperforms other models in terms of root mean square error and Pearson correlation coefficient.
流感的早期预测对于将其造成的损害降到最低至关重要，因为它提供了制定预防措施所需的资源和时间。与传统的机械方法相比，基于深度学习/机器学习的模型通过有效地处理各种数据（如天气和互联网数据），展示了出色的预测性能。然而，由于此类数据的可用性和可靠性有限，许多预测模型仅使用历史发生数据，并将流感预测制定为多元时间序列任务。最近，注意力机制已被用于通过选择输入数据中的有价值数据并赋予它们高权重来解决此问题。特别是，自注意力机制通过利用输入数据中描述目标对象的对象之间的预测关系，在各种预测任务中显示出其潜力。因此，在这项研究中，我们提出了一种基于自注意力的区域流感预测模型，称为 SAIFlu-Net。该模型利用长短时记忆网络提取每个区域的时间序列模式，以及自注意力机制来发现发生模式之间的相似性。为了评估其性能，我们使用现有的预测模型和每周的区域流感数据集进行了广泛的实验。结果表明，该模型在均方根误差和皮尔逊相关系数方面优于其他模型。
