Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.

ABSTRACT: Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoup

SUBMITTER: Aksman LM 

PROVIDER: S-EPMC6679792 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature