The Application of Machine Learning Techniques as an Adjunct to Clinical Decision Making in Alcohol Dependence Treatment

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...

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Bibliographic Details
Published in:Substance use & misuse 2007-01, Vol.42 (14), p.2193-2206
Main Authors: Connor, J. P., Symons, M., Feeney, G. F. X., Young, R. McD, Wiles, J.
Format: Article
Language:English
Subjects:
Abstinence
Addiction
Addictive behaviors
Adult and adolescent clinical studies
Alcohol dependence
Alcohol Deterrents - administration & dosage
Alcohol Deterrents - therapeutic use
alcohol treatment
Alcoholism
Alcoholism - prevention & control
Alcoholism - therapy
Alcoholism and acute alcohol poisoning
Artificial Intelligence
Bayes Theorem
Bayesian
Biological and medical sciences
Clinical decision making
Cognitive Therapy
Decision Support Systems, Clinical
decision tree
Decision Trees
Discriminant Analysis
Evaluation
Fundamental and applied biological sciences. Psychology
Hawaii
Humans
Linear Analysis
machine learning
Machinery
Medical sciences
Outcome Assessment (Health Care) - methods
Pilot Projects
Psychoanalysis
Psychology. Psychoanalysis. Psychiatry
Psychopathology. Psychiatry
Quality of Life
Relapse
Secondary Prevention
Taurine - administration & dosage
Taurine - analogs & derivatives
Taurine - therapeutic use
Toxicology
Treatment
Treatment Outcomes
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Summary: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.
ISSN:1082-6084
1532-2491
DOI:10.1080/10826080701658125