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

Journal Article (Journal Article)

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, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Full Text

Duke Authors

Cited Authors

  • Zheng, H; Petrella, JR; Doraiswamy, PM; Lin, G; Hao, W; Alzheimer’s Disease Neuroimaging Initiative,

Published Date

  • September 8, 2022

Published In

Volume / Issue

  • 5 / 1

Start / End Page

  • 137 -

PubMed ID

  • 36076010

Pubmed Central ID

  • PMC9458727

Electronic International Standard Serial Number (EISSN)

  • 2398-6352

Digital Object Identifier (DOI)

  • 10.1038/s41746-022-00632-7


  • eng

Conference Location

  • England