Title: Machine Learning–Driven Modeling for Personalized Predictions in Alzheimer’s Disease

Speaker: Wenrui Hao(Pennsylvenia State University)

Abstract: Alzheimer’s disease (AD) is a complex and heterogeneous neurodegenerative disorder, where individual patients can exhibit markedly different biomarker trajectories, disease progression rates, and treatment responses. Traditional modeling approaches have provided important insights into the pathophysiology of AD but often fail to capture its patient-specific variability. In this talk, I will present our recent efforts to integrate machine learning with mechanistic mathematical models to advance personalized predictions in AD. By combining mechanistic modeling, statistical inference, and modern AI techniques, we aim to develop digital twins of AD patients—computational surrogates capable of simulating disease progression and exploring individualized treatment strategies. This framework not only enhances our understanding of the AD biomarker cascade but also offers a pathway toward precision medicine approaches that could reduce clinical trial costs and accelerate therapeutic development.