Ozdemir Cagri, Olaimat Mohammad Al, Bozdag Serdar
Department of Computer Science and Engineering, University of North Texas, TX, USA,
Department of Computer Science and Engineering, University of North Texas, TX, USA.
Pac Symp Biocomput. 2025;30:675-689. doi: 10.1142/9789819807024_0048.
Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is generally considered as an intermediate phase between normal cognitive aging and more severe conditions such as AD. Although not all individuals with MCI will develop AD, they are at an increased risk of developing AD. Diagnosing AD once strong symptoms are already present is of limited value, as AD leads to irreversible cognitive decline and brain damage. Thus, it is crucial to develop methods for the early prediction of AD in individuals with MCI. Recurrent Neural Networks (RNN)-based methods have been effectively used to predict the progression from MCI to AD by analyzing electronic health records (EHR). However, despite their widespread use, existing RNN-based tools may introduce increased model complexity and often face difficulties in capturing long-term dependencies. In this study, we introduced a novel Dynamic deep learning model for Early Prediction of AD (DyEPAD) to predict MCI subjects' progression to AD utilizing EHR data. In the first phase of DyEPAD, embeddings for each time step or visit are captured through Graph Convolutional Networks (GCN) and aggregation functions. In the final phase, DyEPAD employs tensor algebraic operations for frequency domain analysis of these embeddings, capturing the full scope of evolutionary patterns across all time steps. Our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets demonstrate that our proposed model outperforms or is in par with the state-of-the-art and baseline methods.
阿尔茨海默病（AD）是一种神经认知障碍，会使记忆力衰退并损害认知功能。轻度认知障碍（MCI）通常被视为正常认知衰老与诸如AD等更严重病症之间的中间阶段。虽然并非所有MCI患者都会发展为AD，但他们患AD的风险会增加。一旦出现明显症状才诊断AD，其价值有限，因为AD会导致不可逆转的认知衰退和脑损伤。因此，开发针对MCI个体的AD早期预测方法至关重要。基于循环神经网络（RNN）的方法已被有效地用于通过分析电子健康记录（EHR）来预测从MCI到AD的进展。然而，尽管它们被广泛使用，但现有的基于RNN的工具可能会增加模型复杂性，并且在捕捉长期依赖性方面常常面临困难。在本研究中，我们引入了一种用于AD早期预测的新型动态深度学习模型（DyEPAD），以利用EHR数据预测MCI受试者向AD的进展。在DyEPAD的第一阶段，通过图卷积网络（GCN）和聚合函数捕捉每个时间步或就诊的嵌入。在最后阶段，DyEPAD采用张量代数运算对这些嵌入进行频域分析，捕捉所有时间步上进化模式的全貌。我们在阿尔茨海默病神经影像倡议（ADNI）和国家阿尔茨海默病协调中心（NACC）数据集上的实验表明，我们提出的模型优于或与现有最先进和基线方法相当。
