Abuhantash Ferial, Abu Hantash Mohd Khalil, Welsch Roy, Seghier Mohamed Lamine, Hadjileontiadis Leontios, Al Shehhi Aamna
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10781747.
Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-class AD classification. Initial steps involve creating a patient-clinical graph network considering latent relationships among cognitive normal (CN), mild cognitive impairment (MCI), and AD patients, followed by training several GNN-based techniques for building prediction models. Incorporating comorbidity data from electronic health records into the feature set yielded the most effective classification results. Notably, the GNN model with attention mechanisms outperforms state-of-the-art techniques in multi-class AD classification, achieving an accuracy = 0.92 [0.91,0.94], AUC = 0.96 [0.95,0.96], and F1-score = 0.92 [0.91,0.94]. This work highlights comorbidity data's impact on AD classification and suggests its potential to deepen disease understanding.
阿尔茨海默病（AD）是最常见的痴呆形式，需要早期预测以便及时干预。利用来自阿尔茨海默病神经影像倡议（ADNI）的数据，我们的研究采用图神经网络（GNN）进行多类AD分类。初始步骤包括创建一个考虑认知正常（CN）、轻度认知障碍（MCI）和AD患者之间潜在关系的患者-临床图网络，随后训练几种基于GNN的技术来构建预测模型。将电子健康记录中的合并症数据纳入特征集产生了最有效的分类结果。值得注意的是，具有注意力机制的GNN模型在多类AD分类中优于现有技术，准确率 = 0.92 [0.91,0.94]，曲线下面积（AUC）= 0.96 [0.95,0.96]，F1分数 = 0.92 [0.91,0.94]。这项工作突出了合并症数据对AD分类的影响，并表明其在深化疾病理解方面的潜力。
