A preliminary risk prediction model for cannabis use disorder.

ABSTRACT: The ongoing trend toward legalization of cannabis for medicinal/recreational purposes is expected to increase the prevalence of cannabis use disorder (CUD). Thus, it is imperative to be able to predict the quantitative risk of developing CUD for a cannabis user based on their personal risk factors. Yet no such model currently exists. In this study, we perform preliminary analysis toward building such a model. The data come from n = 94 regular cannabis users recruited from Albuquerque, New Mexico during 2007-2010. As the data are cross-sectional, we only consider risk factors that remain relatively stable over time. We apply statistical and machine learning classification techniques that allow n to be small relative to the number of predictors. We use predictive accuracy estimated using lea

SUBMITTER: Rajapaksha RMDS 

PROVIDER: S-EPMC7649639 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature