Using machine learning to predict heavy drinking during outpatient alcohol treatment.

ABSTRACT:

Background

Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment.

Method

We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment,

SUBMITTER: Roberts W 

PROVIDER: S-EPMC9180421 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature