## 2 Related Works  
Deep learning is a rapidly advancing field that has gained wide application in recent years. Research teams are actively exploring its use in addressing issues related to Alzheimer's disease progression modeling (DPM). DPM involves predicting the time of clinical state transitions within the AD spectrum, indicating when patients progress to the next stage, as well as changes in AD-related features. Predicting clinical state transition time is crucial for DPM as it facilitates early identification of AD patients and provides information about disease progression and severity. Furthermore, studying AD-related feature changes helps decision makers better understand the trends in disease development. Over time, AD features exhibit varying degrees of change, such as accelerated brain volume reduction and decreased cognitive scores. By capturing these changes in longitudinal data, one can enhance the understanding of the underlying temporal characteristics of the disease. Significant progress has been made in this area. For instance, [PERSON] et al. [9] utilized six volumetric magnetic resonance imaging (MRI) biomarkers to establish a model based on LSTM for predicting the progression of AD. [PERSON] et al. [10] proposed a multi-modal sequence learning model which provides a new solution for handling incomplete data while modeling AD disease progression. As crucial components of DPM and clinical applications, cognitive scores and disease clinical diagnosis have attracted significant attention from researchers. [PERSON] et al. [11] proposed a personalized model based on the Gaussian process to forecast the progression of ADAS-Cog13 scores. [PERSON] et al. [12] examined the correlation between ADAS-Cog13 and brain image characteristics by employing a 3D convolutional neural network. [PERSON] et al. [7] developed a variant of the recurrent network which extracts features from multiple data sources, such as clinical assessments and brain imaging, to predict patients' cognitive abilities and biomarker changes. [PERSON] et al. [6] developed a novel multi-task learning framework that provides more accurate results for disease diagnosis, cognitive scores, and biomarker prediction. [PERSON] et al. [13] utilized MinimalRNN for modeling and introduced the method of model filling to handle missing data. [PERSON] et al. [14], inspired by GradNorm [15], addressed the issue of determining the loss weights for each task in multi-task learning by proposing the Dynamic Weight Average (DWA) method. This approach dynamically adjusts the loss weights of each task during each training iteration, ensuring equal importance across different tasks. The DWA method has been successfully applied in various domains, including fault diagnosis [16] and weather recognition [17]. Furthermore, despite the remarkable achievements of existing models, decision-makers find it challenging to wholeheartedly trust the outcomes due to the opaque nature of deep learning algorithms. Thus, in domains with high safety requirements such as modern healthcare, there is an urgent need to establish an interpretable model that can quantify the reliability of prediction results. Uncertainty theory serves as a method for assessing the credibility of model outputs, and common techniques for uncertainty modeling include Bayesian neural networks [18], dropout [19], and deep ensemble [20]. However, these approaches possess inherent limitations; Bayesian neural networks incur significant computational costs during operations, while dropout and deep ensemble require substantial additional time for sampling and model training. In recent years, the evidential deep learning (EDL) theory introduced by [PERSON] et al. and [PERSON] et al. [21; 22] has exhibited great potential in the field of uncertainty. It directly models the uncertainty of outcomes by placing evidence distributions on the original likelihood function. This methodology eliminates the need for integration or sampling.