Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status

Abstract

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. At the level of clinical practice, distinguishing between treatment- and nontreatment-seekers may help facilitate a targeted treatment approach. Previous efforts to understand the differences between these populations of individuals with AUD leveraged the multidimensional data collected in clinical research settings for AUD that are not well suited to traditional regression methods.Added Value of This Study: Alternating decision trees are well suited to deep-phenotyping data collected in clinical research settings as this approach handles nonparametric, skewed, and missing data whose relationships are nonlinear. This approach has proved to be superior in some cases to conventional clinical methods to solve diagnostic problems in medicine. We used a decision tree classifier to understand treatment- and non-treatment seeking group differences. The decision tree classifier approach chose a subset of factors arranged in an alternating decision tree that best predicts a given outcome. Assuming that the input measures are clinically relevant, the alternating decision tree that is generated has clinical value. Unlike other machine learning approaches, in addition to its predictive value, the nodes in the tree and their arrangement in a hierarchy have clinical utility. With the "if-then" logic of the tree, the clinician can learn what features become important and which recede in importance as the logic of the tree is followed. The decision tree classifier approach reduced 178 characterization measures (both categorical and continuous) in multiple domains to a decision tree comprised of 10 measures that together best classified subjects by treatment seeking status (yes/no).Implications After All the Available Evidence: We leveraged a large data set comprised of 178 clinical measures and using the decision tree approach, we have reduced these to a subset of 10 measures that accurately classified individuals with alcohol dependence by treatment utilization. From this analysis, drinking behavior variables and depression measures are strong treatment seeking predictors. Having identified a cluster of factors that predicts treatment seeking, we can assess the influence of these factors directly on the clinical study outcome measures themselves. In clinical practice these factors can be separate targets for treatment. In clinical research, the group differences my directly influence research outcomes for treatment of AUD.