Many factors have been proposed as contributors to risk of alcohol abuse, but quantifying their influence has been difficult; here a longitudinal study of a large sample of adolescents and machine learning are used to generate models of predictors of current and future alcohol abuse, assessing the relative contribution of many factors, including life history, individual personality differences, brain structure and genotype.
- Robert Whelan
- Richard Watts
- Veronika Ziesch.