Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach.

ABSTRACT: Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed w

SUBMITTER: Kinreich S 

PROVIDER: S-EPMC7960734 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature