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Hugging Face Trending Papers

Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data

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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.

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