AD 研究者终于有了一个能处理稀疏数据、支持个性化轨迹预测的工具——做疾病建模或临床试验设计的团队可以直接用这个框架做 scenario 分析，比传统群体模型更贴近真实临床场景。
阿尔茨海默病进展高度异质且数据稀疏不规则，现有机器学习方法多聚焦静态分类或群体风险估计，缺乏个体化建模和不确定性推理。该研究提出一种个性化数字孪生框架，整合互补建模策略捕捉临床转换和时间依赖，利用ADNI数据集预测认知状态和诊断类别，并量化预测不确定性。评估显示，基于相邻访视的转换建模比序列建模预测精度更高，表明局部转换建模在数据稀疏场景下更高效。该框架支持患者特异性“what-if”轨迹分析，为神经退行性疾病的个性化预测提供了实用且可解释的方法。
Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning. To address these limitations, we present a personalised digital twin framework for AD prediction and scenario-based analysis using multimodal longitudinal data. The proposed approach integrates complementary modelling strategies to capture clinical transitions and temporal dependencies across visits. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitive assessments, clinical variables, and MRI-derived phenotypes, the framework predicts cognitive status and diagnostic categories while quantifying predictive uncertainty and enabling patient-specific what-if trajectory analysis. Evaluation on leak-free subject-level splits demonstrates strong performance in score forecasting and diagnosis classification. In this sparse and irregular ADNI setting, transition-based modelling of adjacent visits achieved higher predictive accuracy than the sequence-based branch, suggesting that local transition modelling may be more data-efficient. While sequence models remain valuable for uncertainty-aware trajectory forecasting, local transition modelling offers a more data-efficient and robust predictive strategy. These findings highlight the importance of aligning temporal modelling strategies with clinical data structure and suggest that transition-based digital twin formulations may provide a practical and interpretable approach for personalised disease forecasting in neurodegenerative disorders.