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