How to remove or control confounds in predictive models, with applications to brain biomarkers.

ABSTRACT:

Background

With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers.

Results

Here we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effect

SUBMITTER: Chyzhyk D 

PROVIDER: S-EPMC8917515 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature