Using machine learning to identify unique predictors of alcohol and cannabis impaired driving.

Two complementary machine learning methods yielded convergent findings on the most salient predictors of impaired driving, increasing confidence in their validity. These methods provide a flexible alternative to traditional models for analyzing high-dimensional data and highlight recent use patterns, substance use disorder symptoms, and age of initiation as key priorities for prevention.