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News for Healthier Living
Machine Learning Models Analyze a Mass of Complex Data to Pick Out Key Predictors of Driving Under the Influence of Alcohol or Cannabis
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. Most research has explored several of these at a time--alcohol use patterns, socioeconomic status, peer influences, and so on-- in analyses that cannot fully capture the multidimensional and interconnected array of influences. For the study in Alcohol: Clinical & Experimental Research, investigators used two machine learning algorithms to identify the most salient predictive factors.
March 11, 2026
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for Animal Vaccines
March 1 2026February 28 2026February 27 2026
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