{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T02:49:30Z","timestamp":1774147770932,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["15H01979"],"award-info":[{"award-number":["15H01979"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Several prognostic models of suicide risk have been published; however, few have been implemented in Japan using longitudinal cohort data. The aim of this study was to identify suicide risk factors for suicidal ideation in the Japanese population and to develop a machine-learning model to predict suicide risk in Japan.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Data was obtained from Wave1 Time 1 (November 2016) and Time 2 (March 2017) of the National Survey for Stress and Health in Japan, were incorporated into a suicide risk prediction machine-learning model, trained using 65 items related to trauma and stress. The study included 3,090 and 2,163 survey respondents &amp;gt;18 years old at Time 1 and Time 2, respectively. The mean (standard deviation, SD) age was 44.9 (10.9) years at Time 1 and 46.0 (10.7) years at Time 2. We analyzed the participants with increased suicide risk at Time 2 survey. Model performance, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were also analyzed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model showed a good performance (AUC = 0.830, 95% confidence interval = 0.795\u20130.866). Overall, the model achieved an accuracy of 78.8%, sensitivity of 75.4%, specificity of 80.4%, positive predictive value of 63.4%, and negative predictive value of 87.9%. The most important risk factor for suicide risk was the participants' Suicidal Ideation Attributes Scale score, followed by the Sheehan Disability Scale score, Patient Health Questionnaire-9 scores, Cross-Cutting Symptom Measure (CCSM-suicidal ideation domain, Dissociation Experience Scale score, history of self-harm, Generalized Anxiety Disorder-7 score, Post-Traumatic Stress Disorder check list-5 score, CCSM-dissociation domain, and Impact of Event Scale-Revised scores at Time 1.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This prognostic study suggests the ability to identify patients at a high risk of suicide using an online survey method. In addition to confirming several well-known risk factors of suicide, new risk measures related to trauma and trauma-related experiences were also identified, which may help guide future clinical assessments and early intervention approaches.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpsyt.2022.918667","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T13:24:44Z","timestamp":1659619484000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["A machine-learning model to predict suicide risk in Japan based on national survey data"],"prefix":"10.3389","volume":"13","author":[{"given":"Po-Han","family":"Chou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shao-Cheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Shin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaru","family":"Horikoshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaya","family":"Ito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1038\/s41746-020-0287-6","article-title":"Machine learning approach predicts future risk to suicidal ideation from social media data","volume":"3","author":"Roy","year":"2020","journal-title":"NPJ Digit Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1037\/bul0000084","article-title":"Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research","volume":"143","author":"Franklin","year":"2017","journal-title":"Psychol Bull."},{"key":"B3","doi-asserted-by":"publisher","first-page":"707916","DOI":"10.3389\/fpsyt.2021.707916","article-title":"Applying machine learning approaches to suicide prediction using healthcare data: overview and future directions","volume":"12","author":"Boudreaux","year":"2021","journal-title":"Front Psychiatry."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1111\/jcpp.12916","article-title":"Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning","volume":"59","author":"Walsh","year":"2018","journal-title":"j Child Psychol Psychiatry."},{"key":"B5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.jad.2018.01.019","article-title":"ten-year prediction of suicide death using cox regression and machine learning in a nationwide retrospective cohort study in South Korea","volume":"231","author":"choi","year":"2018","journal-title":"J Affect Disord."},{"key":"B6","doi-asserted-by":"publisher","first-page":"e211428","DOI":"10.1001\/jamanetworkopen.2021.1428","article-title":"Prospective validation of an electronic health record-based, real-time suicide risk model","volume":"4","author":"Walsh","year":"2021","journal-title":"JAMA network open."},{"key":"B7","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1038\/s41398-020-01100-0","article-title":"Machine learning for suicide risk prediction in children and adolescents with electronic health records","volume":"10","author":"Su","year":"2020","journal-title":"Transl Psychiatry."},{"key":"B8","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/s41380-019-0531-0","article-title":"Suicide prediction models: a critical review of recent research with recommendations for the way forward","volume":"25","author":"Kessler","year":"2020","journal-title":"Mol Psychiatry."},{"key":"B9","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1176\/appi.ajp.159.6.909","article-title":"Contact with mental health and primary care providers before suicide: a review of the evidence","volume":"159","author":"Luoma","year":"2002","journal-title":"Am J Psychiatry."},{"key":"B10","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1001\/jamapsychiatry.2020.4165","article-title":"la Garza A, Blanco C, Olfson M, Wall MM. 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