Data-driven causal model discovery and personalized prediction in Alzheimer's disease.

ABSTRACT: With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning

SUBMITTER: Zheng H 

PROVIDER: S-EPMC9458727 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature