Connor J P, Symons M, Feeney G F X, Young R McD, Wiles J
Discipline of Psychiatry, The University of Queensland, Brisbane, Australia.
Subst Use Misuse. 2007;42(14):2193-206. doi: 10.1080/10826080701658125.
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.
除了少数例外情况，成瘾科学领域的研究一直依赖于线性统计和方法。成瘾涉及一系列复杂的非线性行为。本研究应用了两种机器学习技术，即贝叶斯和决策树分类器，来评估酒精依赖治疗项目的结果。将这些非线性方法与标准线性分析进行了比较。本研究纳入了73名接受为期12周认知行为疗法（CBT）项目的酒精依赖受试者，以及66名接受相同项目但同时还服用预防复发药物阿坎酸的受试者。使用基线时的人口统计学、饮酒情况、依赖严重程度、渴望程度、与健康相关的生活质量和心理测量指标来预测12周时的戒酒情况。决策树在两个数据集上的预测准确率为77%，贝叶斯网络为73%，判别分析为42%。结合临床经验，机器学习方法在理解基于戒酒的酒精治疗项目治疗结果背后的复杂关系方面具有前景。
