Who is alcohol cue-reactive? A machine learning approach.

ABSTRACT:

Background

The alcohol cue-exposure paradigm is widely used in alcohol use disorder (AUD) research. Individuals with AUD exhibit considerable variability in their alcohol cue-reactivity, highlighting the need to identify characteristics that contribute to this heterogeneity. This study applied machine learning models to identify clinical and sociodemographic predictors of subjective alcohol cue-reactivity (ALCUrge).

Methods

Individuals with AUD (N = 139; 83 M/56F) completed an alcohol cue-exposure paradigm and a battery of clinical and sociodemographic measures. ALCUrge (primary outcome variable) was assessed using the Alcohol Urge Questionnaire following alcohol cue-exposure. We implemented three machine learning models (Lasso regression, Ridge regression, Random Forest) to

SUBMITTER: Kirsch DE 

PROVIDER: S-EPMC12368849 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature