BACKGROUND Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. The aim of this paper is to develop a machine learning approach to predict adults at risk for OUD and to identify interactions between various characteristics that increase this risk. METHODS In this approach, a data set was curated using the responses from the 2016 edition of the National Survey on Drug Use and Health (NSDUH). Using this data set, tree-based classifiers (decision tree and random forest) were trained, while employing downsampling to handle class imbalance. Predictions from the tree-based classifiers were also compared to the results from a logistic regression model. The results from the three classifiers were then interpreted synergistically to highlight individual characteristics and their interplay that pose a risk for OUD. RESULTS Random forest predicted adults at risk for OUD with remarkable accuracy, with the average area under the Receiver-Operating-Characteristics curve (AUC) over 0.89, even though the prevalence of OUD was only about 1 %. It showed a slight improvement over logistic regression. Logistic regression identified statistically significant characteristics, while random forest ranked the predictors in order of their contribution to OUD prediction. Early initiation of marijuana (before 18 years) emerged as the dominant predictor. Decision trees revealed that early marijuana initiation especially increased the risk if individuals: (i) were between 18-34 years of age, or (ii) had incomes less than $49,000, or (iii) were of Hispanic and White heritage, or (iv) were on probation, or (v) lived in neighborhoods with easy access to drugs. CONCLUSIONS Machine learning can accurately predict adults at risk for OUD, and identify interactions among the factors that pronounce this risk. Curbing early initiation of marijuana may be an effective prevention strategy against opioid addiction, especially in high risk groups.

中文翻译：

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使用基于树的分类器了解阿片类药物使用障碍（OUD）。

背景技术阿片类药物使用障碍（OUD）是一种对阿片类药物的生理或心理依赖，是一种公共卫生流行病。确定可能发展为OUD的成年人可以帮助公共卫生官员规划有效的干预策略。本文的目的是开发一种机器学习方法，以预测有OUD风险的成年人，并识别增加这种风险的各种特征之间的相互作用。方法在这种方法中，使用2016年《美国毒品和健康调查》（NSDUH）的答复来整理数据集。使用该数据集，训练了基于树的分类器（决策树和随机森林），同时采用降采样处理类不平衡。还将基于树的分类器的预测与逻辑回归模型的结果进行了比较。然后，对三个分类器的结果进行协同解释，以突出显示可能造成OUD风险的各个特征及其相互作用。结果随机森林预测成年人有OUD风险的准确性非常高，尽管OUD的患病率仅为1％，但其平均面积在接收器-操作特征曲线（AUC）下仍超过0.89。与logistic回归相比，它显示出轻微的改进。Logistic回归确定了统计学上的显着特征，而随机森林则按预测因子对OUD预测的贡献顺序对预测因子进行排名。大麻的早期发作（18岁之前）成为主要的预测因子。决策树显示，如果个人（i）年龄在18-34岁，或（ii）收入低于$ 49,000，或（iii）具有西班牙裔和白人遗产，或（iv）处于缓刑状态，或（v）居住在容易获得毒品的社区。结论机器学习可以准确地预测成年人发生OUD的风险，并确定表明该风险的因素之间的相互作用。遏制尽早提倡使用大麻可能是预防阿片类药物成瘾的有效策略，尤其是在高风险人群中。