Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
2026-06-08 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors developed a personalized digital twin system to predict how Alzheimer's disease progresses in individual patients using different types of medical data collected over time. Their method focuses on modeling changes between doctor visits, which worked better with the limited and irregular data than methods looking at the full patient history all at once. This approach can also show how confident the system is in its predictions and allows exploring 'what-if' scenarios for individual patients. Their results suggest this transition-based modeling is a practical way to support personalized disease tracking and decision-making in Alzheimer's care.
Alzheimer's diseasedigital twinlongitudinal datatransition modelingcognitive assessmentMRIpredictive uncertaintypersonalized medicineneurodegenerative disordersmachine learning
Authors
Yinyu Huang, Yilin Zhang, Sofia Michopoulou, Christopher Kipps, Rahman Attar
Abstract
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.