{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:48:52Z","timestamp":1786978132252,"version":"build-2736575974"},"reference-count":45,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2017,7,25]],"date-time":"2017-07-25T00:00:00Z","timestamp":1500940800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["1R01MH109507\u201001"],"award-info":[{"award-number":["1R01MH109507\u201001"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Traumatic Stress"],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>\n                    Suicide rates among recent veterans have led to interest in risk identification. Evidence of gender\u2010and trauma\u2010specific predictors of suicidal ideation necessitates the use of advanced computational methods capable of elucidating these important and complex associations. In this study, we used machine learning to examine gender\u2010specific associations between predeployment and military factors, traumatic deployment experiences, and psychopathology and suicidal ideation (SI) in a national sample of veterans deployed during the Iraq and Afghanistan conflicts (\n                    <jats:italic>n<\/jats:italic>\n                    = 2,244). Classification, regression tree analyses, and random forests were used to identify associations with SI and determine their classification accuracy. Findings converged on several associations for men that included depression, posttraumatic stress disorder (PTSD), and somatic complaints. Sexual harassment during deployment emerged as a key factor that interacted with PTSD and depression and demonstrated a stronger association with SI among women. Classification accuracy for SI presence or absence was good based on the receiver operating characteristic area under the curve, men = .91, women = .92. The risk for SI was classifiable with good accuracy, with associations that varied by gender. The use of machine learning analyses allowed for the discovery of rich, nuanced results that should be replicated in other samples and may eventually be a basis for the development of gender\u2010specific actuarial tools to assess SI risk among veterans.\n                  <\/jats:p>","DOI":"10.1002\/jts.22210","type":"journal-article","created":{"date-parts":[[2017,7,25]],"date-time":"2017-07-25T07:12:46Z","timestamp":1500966766000},"page":"362-371","source":"Crossref","is-referenced-by-count":52,"title":["Gender Differences in Machine Learning Models of Trauma and Suicidal Ideation in Veterans of the Iraq and Afghanistan Wars"],"prefix":"10.1002","volume":"30","author":[{"given":"Jaimie L.","family":"Gradus","sequence":"first","affiliation":[{"name":"National Center for PTSD and VA Boston Healthcare System Boston Massachusetts USA"},{"name":"Department of Psychiatry Boston University School of Medicine Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew W.","family":"King","sequence":"additional","affiliation":[{"name":"National Center for PTSD and VA Boston Healthcare System Boston Massachusetts USA"},{"name":"Department of Psychiatry Boston University School of Medicine Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac","family":"Galatzer\u2010Levy","sequence":"additional","affiliation":[{"name":"Department of Psychiatry New York University School of Medicine New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy E.","family":"Street","sequence":"additional","affiliation":[{"name":"National Center for PTSD and VA Boston Healthcare System Boston Massachusetts USA"},{"name":"Department of Psychiatry Boston University School of Medicine Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,7,25]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890423349"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0749-3797(18)30622-6"},{"key":"e_1_2_5_4_1","unstructured":"Arlinghaus K. A. &Hickey J. A.(2005).TBI quick screen for a veteran population. Unpublished measure."},{"key":"e_1_2_5_5_1","first-page":"1","article-title":"Using data mining to explore complex clinical decisions: A study of hospitalization after a suicide attempt","volume":"67","author":"Baca\u2010Garcia E.","year":"2006","journal-title":"Journal of Clinical Psychiatry"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/070674370905400207"},{"key":"e_1_2_5_7_1","volume-title":"A review of suicide assessment measures for intervention research with adults and older adults","author":"Brown G. K.","year":"2001"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2015.01.030"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9343(87)90061-1"},{"key":"e_1_2_5_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.08.033"},{"key":"e_1_2_5_11_1","unstructured":"Department of Veterans Affairs\/Department of Defense. 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