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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\u2010 and cannabis\u2010impaired driving from a wide range of candidate variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data came from annual cross\u2010sectional surveys of 18\u2010 to 25\u2010year\u2010olds participating in the Washington Young Adult Health Survey (2015\u20132022). Analyses were limited to two overlapping subsets of participants: those who reported past\u2010month alcohol use for analyses predicting alcohol\u2010impaired driving (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20099852) and those who reported past\u2010month cannabis use for analyses predicting cannabis\u2010impaired driving (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20094891). 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\u2010dimensional data with potentially collinear predictors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For likelihood of alcohol\u2010impaired driving, top predictors included alcohol use frequency, participants' age, peak drinking quantity, age of alcohol initiation, full\u2010time employment, and cannabis use frequency. For likelihood of cannabis\u2010impaired driving, top predictors included cannabis use frequency, cannabis\u2010related memory problems, simultaneous alcohol and cannabis use frequency, increased cannabis tolerance, and age of cannabis initiation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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\u2010dimensional data and highlight recent use patterns, substance use disorder symptoms, and age of initiation as key priorities for prevention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/acer.70245","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T15:00:51Z","timestamp":1773241251000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using machine learning to identify unique predictors of alcohol and cannabis impaired driving"],"prefix":"10.1111","volume":"50","author":[{"given":"Brian H.","family":"Calhoun","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7181-5050","authenticated-orcid":false,"given":"Brittney A.","family":"Hultgren","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Connor J.","family":"McCabe","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9731-8776","authenticated-orcid":false,"given":"Isaac C.","family":"Rhew","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary E.","family":"Larimer","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason R.","family":"Kilmer","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarina","family":"Guttmannova","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1037\/a0030877"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addbeh.2012.12.017"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/10826084.2017.1327979"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2011.629700"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2022.2124803"},{"key":"e_1_2_8_8_1","unstructured":"Centers for Disease Control and Prevention. 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