Using machine learning to examine predictors of treatment goal change among individuals seeking treatment for alcohol use disorder.

ABSTRACT:

Introduction

The goals of individuals seeking treatment for alcohol use disorder (AUD) are typically quantified as abstinent or nonabstinent (e.g., moderate drinking) goals. However, treatment goals can vary over time and be influenced by life circumstances. This study aims to identify predictors of treatment goal change and direction of change from baseline to six-month follow-up among individuals seeking treatment for AUD.

Methods

This study is a secondary analysis of data from the Relapse Replication and Extension Project. The study included participants who completed assessments at baseline and six-month follow-up in the analysis (n = 441). We used decision trees to examine 111 potential predictors of treatment goal change. The study cross-validated results using random

SUBMITTER: Schwebel FJ 

PROVIDER: S-EPMC9708382 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature