Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity
Background This longitudinal study explored the utility of machine learning (ML)
methodology in predicting the trajectory of severity of substance use from childhood to thirty
years of age using a set of psychological and health characteristics. Design Boys (N= 494)
and girls (N= 206) were recruited using a high-risk paradigm at 10–12 years of age and
followed up at 12–14, 16, 19, 22, 25 and 30 years of age. Measurements At each visit, the
subjects were administered a comprehensive battery to measure psychological makeup …
methodology in predicting the trajectory of severity of substance use from childhood to thirty
years of age using a set of psychological and health characteristics. Design Boys (N= 494)
and girls (N= 206) were recruited using a high-risk paradigm at 10–12 years of age and
followed up at 12–14, 16, 19, 22, 25 and 30 years of age. Measurements At each visit, the
subjects were administered a comprehensive battery to measure psychological makeup …
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