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Machine learning models identify key predictors of driving under the influence of alcohol or cannabis

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The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary factors contributing to driving under the influence, according to a new analysis using machine learning. Impaired driving is known to be influenced by a range of behavioral, demographic, and contextual factors.

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