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

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Τίτλος: Using machine learning to identify unique predictors of alcohol and cannabis impaired driving.
Συγγραφείς: Calhoun BH; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA., Hultgren BA; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA., McCabe CJ; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA., Rhew IC; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA., Larimer ME; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA., Kilmer JR; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA., Guttmannova K; Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, Washington, USA.
Πηγή: Alcohol, clinical & experimental research [Alcohol Clin Exp Res (Hoboken)] 2026 Mar; Vol. 50 (3), pp. e70245.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Periodicals Country of Publication: United States NLM ID: 9918609780906676 Publication Model: Print Cited Medium: Internet ISSN: 2993-7175 (Electronic) Linking ISSN: 29937175 NLM ISO Abbreviation: Alcohol Clin Exp Res (Hoboken) Subsets: MEDLINE
Imprint Name(s): Original Publication: Hoboken, NJ : Wiley Periodicals, [2023]-
Ιατρικοί όροι (MeSH): Driving Under the Influence*/statistics & numerical data , Alcohol Drinking*/epidemiology , Machine Learning* , Automobile Driving*, Washington/epidemiology ; Humans ; Cross-Sectional Studies ; Young Adult ; Female ; Adolescent ; Adult ; Predictive Learning Models ; Risk Factors ; Prediction Algorithms ; Random Forest ; Classification Algorithms
Περίληψη: Background: Alcohol- and cannabis-impaired driving remain major public health concerns, particularly among young adults. Although prior studies have identified numerous risk factors, most have focused on limited subsets of predictors, restricting a broader understanding of impaired driving. This study applied machine learning to identify salient predictors of alcohol- and cannabis-impaired driving from a wide range of candidate variables.
Methods: Data came from annual cross-sectional surveys of 18- to 25-year-olds participating in the Washington Young Adult Health Survey (2015-2022). Analyses were limited to two overlapping subsets of participants: those who reported past-month alcohol use for analyses predicting alcohol-impaired driving (N = 9852) and those who reported past-month cannabis use for analyses predicting cannabis-impaired driving (N = 4891). Regularized regression and random forests were used to identify the most salient predictors of each type of impaired driving from a large set of approximately 80 candidate variables. These methods were selected for their complementary strengths and their shared capacity for robust performance when handling high-dimensional data with potentially collinear predictors.
Results: For likelihood of alcohol-impaired driving, top predictors included alcohol use frequency, participants' age, peak drinking quantity, age of alcohol initiation, full-time employment, and cannabis use frequency. For likelihood of cannabis-impaired driving, top predictors included cannabis use frequency, cannabis-related memory problems, simultaneous alcohol and cannabis use frequency, increased cannabis tolerance, and age of cannabis initiation.
Conclusions: 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.
(© 2026 Research Society on Alcohol.)
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Grant Information: R00AA030052 United States AA NIAAA NIH HHS; R00 AA030052 United States AA NIAAA NIH HHS; Washington State Health Care Authority (Division of Behavioral Health and Recovery); R01DA057705 United States DA NIDA NIH HHS; R01 DA057705 United States DA NIDA NIH HHS
Contributed Indexing: Keywords: driving under the influence; impaired driving; machine learning; random forest; regularization
Entry Date(s): Date Created: 20260311 Date Completed: 20260710 Latest Revision: 20260710
Update Code: 20260711
PubMed Central ID: PMC12981337
DOI: 10.1111/acer.70245
PMID: 41811226
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2993-7175
DOI:10.1111/acer.70245