A computer model has been developed to identify teenagers at risk of alcohol misuse later in life.
The model analyses an adolescent’s neurobiological, psychological and environmental influences, including traumatic events and family history, to determine whether he or she is at risk.
This finding, reported in the journal Nature, highlights the importance of various contributors to risk of alcohol abuse and suggests individualised targets for prevention.
Slightly more than 40% of 13-to–14 year-old adolescents in the United States report alcohol use, with 10% exhibiting regular use.
These figures are of concern because early alcohol use is a strong risk factor for alcohol dependence in adulthood.
Various factors are known to contribute to greater risk of alcohol abuse, including painful life events, environmental influences and candidate genes, but their relative importance has been difficult to quantify.
Robert Whelan of University College Dublin, Ireland, and colleagues used machine learning to analyse data on neurobiological, life experiences and environmental influences from almost 700 adolescents.
This created a system which learns from the data to generate models of current and future adolescent alcohol misuse.
The authors report that factors that make greater contributions to individual differences in vulnerability to alcohol misuse can be identified, and this may shed light on some causes of early alcohol misuse.
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