A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

ABSTRACT:

Aims

Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches.

Methods

Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were enter

SUBMITTER: Coughlin LN 

PROVIDER: S-EPMC7297111 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature