用多模态数据构建个性化认知衰退预测模型，提升临床可靠性。
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
- 融合脑影像、生物标志物等多源数据，用Transformer与深度马尔可夫模型建模。
- 在1666名患者上实现高精度预测，对不同人群公平且抗数据缺失。
- 适合用于阿尔茨海默病临床试验筛选和个体化诊疗规划。
由于阿尔茨海默病（AD）进展异质性，预测个体认知衰退极具挑战。可靠的临床工具需兼具高准确率、跨人群公平性及对缺失数据的鲁棒性。本文提出CognitiveTwin，一种数字孪生框架，用于预测患者特异性认知轨迹。该模型整合了纵向多模态数据（认知评分、磁共振成像、正电子发射断层扫描、脑脊液生物标志物及遗传信息）。采用基于Transformer的架构融合多模态特征，并使用深度马尔可夫模型捕捉时序动态。在来自TADPOLE（阿尔茨海默病神经影像计划）数据集的1,666名患者上进行训练与评估。模型在预测误差、人口统计学公平性以及对缺失非随机（MNAR）数据模式的鲁棒性方面均表现优异。CognitiveTwin能提供精准且个性化的认知衰退预测，其跨人群公平性与对临床脱落的韧性使其成为临床试验富集与个性化护理规划的可靠工具。
原文摘要 · Abstract (English)
Predicting individual cognitive decline in Alzheimer's 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's 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.
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