Artificial neural networks for adolescent marijuana use and clinical features of marijuana dependence

This article compares the performance of multiple logistic regression (MLR) with feed-forward, artificial neural network (ANN) models for the assessment of adolescent marijuana use and clinical features of dependence based on self-evaluation from recent National Household Surveys on Drug Abuse (NHSDA). The effect of training and testing the neural networks with randomly selected data was compared to data selected as a function of survey year. The technical aim of the study was to account for adolescent marijuana use and features of marijuana dependence based on experiences with alcohol and tobacco. Similarities observed in MLR and ANN model performance may indicate no major complex or nonlinear relationships in cross-sectional epidemiological data selected to model adolescent drug use and dependence in this specific application. We concluded that ANNs should be further studied in future longitudinal research, perhaps with modeling of recursive networks, allowing feedback from drug dependence to levels of marijuana use. The ANN models also have the potential to model drug use and dependence based on input parameters with no obvious direct link to drug involvement-e.g., polymorphisms associated with "openness to experience" or other personality traits hypothesized to function as distal antecedents, and could thus be implemented to identify higher risk youths using assessments indirectly related or nonlinearly associated to adolescent drug use and dependence but less sensitive to survey-related response tendencies.