由于疾病进展的异质性，预测阿尔茨海默的疾病(AD)的个体认知能力下降很困难。可靠的临床工具不仅需要高精度，还需要跨人口统计的公平性以及对缺失数据的稳健性。我们提出了 CognitiveTwin, 一个数字孪生框架，可以预测患者特定的认知轨迹。该模型整合了多模态纵向数据(认知评分,磁共振成像,正电子发射断层扫描,脑脊液生物标志物,和遗传学)。我们使用基于 Transformer 的架构来融合这些模式，并使用深度马尔可夫模型来捕获时间动态。我们使用 TADPOLE (Alzheimer的 疾病神经影像计划) 数据集中 1,666 患者的数据来训练和评估该框架。我们评估了模型的预测误差, 人口统计公平性, 以及对非随机缺失 (MNAR) 数据模式的稳健性。 oggnitiveTwin 提供准确且个性化的认知衰退预测。它在患者人口统计数据中表现出的公平性和对临床退出的弹性使其成为丰富临床试验和个性化护理计划的可靠工具。
Predicting individual cognitive decline in Alzheimer的 disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories. The model integrates multi-modal longitudinal data (cognitive scores, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetics). We use a Transformer-based architecture to fuse these modalities and a Deep Markov Model to capture temporal dynamics. We trained and evaluated the framework using data from 1,666 patients in the TADPOLE (Alzheimer的 Disease Neuroimaging Initiative) dataset. We assessed the model for prediction error, demographic fairness, and robustness to missing-not-at-random (MNAR) data patterns. ognitiveTwin provides accurate and personalized predictions of cognitive decline. Its demonstrated fairness across patient demographics and resilience to clinical dropout make it a reliable tool for clinical trial enrichment and personalized care planning.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)