The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment.
Subst Use Misuse
; 42(14): 2193-206, 2007.
Article
em En
| MEDLINE
| ID: mdl-18098000
ABSTRACT
With few exceptions, research in the addictive sciences has relied on linear statistics and methodologies. Addiction involves a complex array of nonlinear behaviors. This study applies two machine learning techniques, Bayesian and decision tree classifiers, in the assessment of outcome of an alcohol dependence treatment program. These nonlinear approaches are compared to a standard linear analysis. Seventy-three alcohol-dependent subjects undertaking a 12-week cognitive-behavioral therapy (CBT) program and 66 subjects undertaking an identical program but also prescribed the relapse prevention agent Acamprosate were employed in this study. Demographic, alcohol use, dependence severity, craving, health-related quality of life, and psychological measures at baseline were used to predict abstinence at 12 weeks. Decision trees had a 77% predictive accuracy across both data sets, Bayesian networks 73%, and discriminant analysis 42%. Combined with clinical experience, machine learning approaches offer promise in understanding the complex relationships that underlie treatment outcome for abstinence-based alcohol treatment programs.
Buscar no Google
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Avaliação de Resultados em Cuidados de Saúde
/
Sistemas de Apoio a Decisões Clínicas
/
Alcoolismo
Idioma:
En
Ano de publicação:
2007
Tipo de documento:
Article