| Abstract: |
| Alzheimer`s disease (AD) is highly heterogeneous, with patients showing different biomarker trajectories, progression rates, and treatment responses. Traditional models offer insight but miss individual variability. We integrate machine learning with mechanistic mathematical modeling to build patient digital twins that simulate disease progression and test personalized therapies. This framework deepens understanding of the biomarker cascade and supports precision medicine, with potential to reduce clinical trial cost and accelerate therapeutic development. |
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