Impaired driving remains a critical safety concern as cannabis legalization expands, yet most risk assessment tools rely on limited predictor sets. This machine learning approach identifies previously unrecognized risk factors that could inform more precise clinical screening and intervention strategies.

This cross-sectional study analyzed 8 years of survey data from Washington state young adults (ages 18-25) who used alcohol (N=9,852) or cannabis (N=4,891) in the past month. Using regularized regression and random forest algorithms, researchers identified salient predictors of impaired driving from comprehensive variable sets. The machine learning approach revealed novel risk factors beyond traditional demographic and substance use patterns, though the study’s observational design limits causal inference and generalizability beyond this specific population.

“While identifying risk factors is valuable, this doesn’t change my clinical approach to cannabis patients regarding driving safety. I still rely on direct patient education about impairment timing and individual response rather than predictive algorithms.”

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