عنوان انگلیسی مقاله:
Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status
ترجمه فارسی عنوان مقاله:
استفاده از یادگیری ماشین برای طبقه بندی افراد مبتلا به اختلال در مصرف الکل بر اساس وضعیت به دنبال یافتن درمان
Sciencedirect - Elsevier - EClinicalMedicine 12 (2019) 70–78
Mary R. Lee a,⁎, Vignesh Sankar a, Aaron Hammer a, William G. Kennedy b, Jennifer J. Barb c, Philip G. McQueen c, Lorenzo Leggio
Objective: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory
measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD)
Method: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADTs were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models.
Results: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy.
Interpretation: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying b7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment. Funding: Funding for this work was provided by the Intramural Research Programs of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA) and the Center for Information Technology (CIT).
Research in Context: Evidence Before This Study: Less than 10% of persons who meet lifetime criteria for Alcohol Use Disorder (AUD) receive treatment. As the etiology of AUD represents a complex interaction between neurobiological, social, environmental and psychological factors, low treatment utilization likely stems from barriers on multiple levels. Given this issue, it is important from both a research and clinical standpoint to determine what characteristics are associated with treatment utilization in addition to merely asking individuals if they wish to enter treatment. At the level of clinical research, if there are phenotypic differences between treatment and nontreatment-seekers that directly influence outcomes of early-phase studies, these phenotypic differences are a potential confound in assessing the utility of an experimental treatment for AUD. ...
Keywords: Machine learning | Treatment utilization | Alcohol Use Disorder