| Abstract: |
| With over 5 million individuals affected in the US, Alzheimer`s disease (AD) has become a pressing concern. Personalized treatment plans for AD patients offer a promising new avenue for managing this disease but require novel approaches for analyzing the increasing amount of electronic brain data available. In this talk, we will introduce a mathematical modeling approach for describing the progression of AD clinical biomarkers and incorporating patient data to enable personalized prediction and optimal treatment. Specifically, we will validate this mathematical model on a multi-institutional dataset of AD biomarkers to provide personalized predictions for AD patients. |
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