{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:59:53Z","timestamp":1785405593916,"version":"3.56.0"},"reference-count":56,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":210,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Depression and Anxiety"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The cooccurrence of posttraumatic stress disorder (PTSD) and opioid use heightens suicide risk. We aimed to develop and validate a machine learning\u2010based suicide prediction model (SPM) to identify PTSD patients prescribed opioids who are at risk of suicide within 6\u2010month prediction intervals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using 2016\u201321 OneFlorida+ data, we compared the predictive performance of multiple models, including least absolute shrinkage and selection operator (LASSO) regression, gradient boosting machines (GBMs), random forest (RF), and deep neural networks (DNNs). The best\u2010performing SPM was selected to predict 6\u2010month suicide risks among adult PTSD patients who received opioids. The index date was the first day when both an opioid prescription and a PTSD diagnosis occurred within 180 days. We divided the 2016\u201318 cohort into training and internal validation datasets (2:1 ratio), applying machine learning models to the training dataset to predict suicide\u2010related outcomes. We evaluated model prediction performance using various metrics on internal (2016\u201318) and external (2019\u201321) validation cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 5578 patients (age\u2009=\u200938.1\u2009\u00b1\u200911.1, female\u2009=\u200983.2%) in the 2016\u201318 cohort, 709 (12.7%) patients had at least one suicide\u2010related outcome during follow\u2010up. The final RF model selected 271\/299 covariates, with C\u2010statistics, accuracy, sensitivity, specificity, and precision being 83.6% (81.7%\u221285.9%), 86.2% (85.2%\u221287.1%), 65.0% (59.8%\u221270.1%), 87.6% (86.7%\u221288.5%), and 26.6% (24.6%\u221228.8%), respectively. We classified intervals into 10 suicide\u2010risk subgroups based on the predicted probabilities, with 79.2% of suicide intervals captured in the top 3 decile subgroups. We observed similar findings in the 2019\u201321 cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20094849), with C\u2010statistics, accuracy, sensitivity, specificity, and precision being 83.6% (82.2%\u221284.9%), 87.0% (86.5%\u221287.6%), 65.3% (61.7%\u221268.7%), 88.2% (87.6%\u221288.7%), and 22.3% (21.1%\u221223.5%), respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our SPM performed well in internal and external validations. It may serve as a feasible tool to identify patients at risk of suicide, helping prioritize preventive interventions, and enabling providers to allocate time and resources more efficiently to patients who may benefit from closer follow\u2010up.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/da\/5305506","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:12:32Z","timestamp":1785402752000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Model for Predicting Suicide Risks Among Patients With Posttraumatic Stress Disorder Who Received Opioids"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2825-9180","authenticated-orcid":false,"given":"Shu","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-8720","authenticated-orcid":false,"given":"Amie J.","family":"Goodin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9732-6781","authenticated-orcid":false,"given":"Jill A.","family":"Star","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9799-2592","authenticated-orcid":false,"given":"Serena Jingchuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2238-5429","authenticated-orcid":false,"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5291-5198","authenticated-orcid":false,"given":"Jie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2608-616X","authenticated-orcid":false,"given":"Tianze","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"e_1_2_12_1_2","unstructured":"Centers for Disease Control and Prevention (U.S.) Web-based Injury Statistics Query and Reporting System (WISQARS) 2026 April 20https:\/\/wisqars.cdc.gov\/."},{"key":"e_1_2_12_2_2","unstructured":"National Institute of Mental Health Suicide 2026 April 20https:\/\/www.nimh.nih.gov\/health\/statistics\/suicide."},{"key":"e_1_2_12_3_2","unstructured":"HedegaardH. 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