عنوان انگلیسی مقاله:
Analysis of substance use and its outcomes by machine learning I: Childhood evaluation of liability to substance use disorder
ترجمه فارسی عنوان مقاله:
تجزیه و تحلیل استفاده از مواد و نتایج آن با یادگیری ماشین I: ارزیابی کودک از مسئولیت در برابر اختلال در مصرف مواد
Sciencedirect - Elsevier - Drug and Alcohol Dependence, 206 (2020) 107605: doi:10:1016/j:drugalcdep:2019:107605
Yankang Jinga,c,1, Ziheng Hua,c,1, Peihao Fana,c, Ying Xuea,c, Lirong Wanga,c, Ralph E. Tarterb, Levent Kiriscib, Junmei Wanga,c,*, Michael Vanyukovb,**, Xiang-Qun Xiea,c,*
Background: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important
to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make
prediction from data. We hypothesized that ML identifies the health, psychological, psychiatric, and contextual
features to predict SUD, and the identified features predict high-risk individuals to develop SUD.
Method: Male (N=494) and female (N=206) participants and their informant parents were administered a
battery of questionnaires across five waves of assessment conducted at 10–12, 12–14, 16, 19, and 22 years of
age. Characteristics most strongly associated with SUD were identified using the random forest (RF)algorithm
from approximately 1000 variables measured at each assessment. Next, the complement of features was validated,
and the best models were selected for predicting SUD using seven ML algorithms. Lastly, area under the
receiver operating characteristic curve (AUROC) evaluated accuracy of detecting individuals who develop SUD
+/- up to thirty years of age.
Results: Approximately thirty variables strongly predict SUD. The predictors shift from psychological dysregulation
and poor health behavior in late childhood to non-normative socialization in mid to late adolescence. In
10–12-year-old youths, the features predict SUD+/- with 74% accuracy, increasing to 86% at 22 years of age.
The RF algorithm optimally detects individuals between 10–22 years of age who develop SUD compared to other
Conclusion: These findings inform the items required for inclusion in instruments to accurately identify high risk
youths and young adults requiring SUD prevention
Keywords: Substance use disorder | Machine learning | Substance abuse prevention | Big data | Screening addiction risk