Journal article
The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment
Substance Use and Misuse, Vol.42(14), pp.2193-2206
2007
PMID: 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.
Details
- Title
- The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment
- Authors
- J P Connor (Author) - University of QueenslandM Symons (Author) - University of QueenslandG F X Feeney (Author) - Princess Alexandra HospitalRoss Young (Author) - Queensland University of TechnologyJ Wiles (Author) - University of Queensland
- Publication details
- Substance Use and Misuse, Vol.42(14), pp.2193-2206
- Publisher
- Taylor & Francis Inc.
- DOI
- 10.1080/10826080701658125
- ISSN
- 1532-2491
- PMID
- 18098000
- Organisation Unit
- Office of the Deputy Vice-Chancellor (Research and Innovation); University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99551005902621
- Output Type
- Journal article
Metrics
16 Record Views
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- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Psychiatry
- Psychology
- Substance Abuse
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Source: InCites