{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:32:56Z","timestamp":1789471976180,"version":"build-2803163510"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018936","name":"Universit\u00e4tsklinikum Heidelberg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018936","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Child Adolesc Psychiatry"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00787-026-02963-2","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:34:28Z","timestamp":1769841268000},"page":"1565-1573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning"],"prefix":"10.1007","volume":"35","author":[{"given":"Christian","family":"Hertel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noel","family":"Strahm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Lerch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corinna","family":"Reichl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Koenig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marialuisa","family":"Cavelti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Kaess","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"2963_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16224581","volume":"16","author":"K-S Lim","year":"2019","unstructured":"Lim K-S, Wong CH, McIntyre RS et al (2019) Global lifetime and 12-month prevalence of suicidal behavior, deliberate self-harm and non-suicidal self-injury in children and adolescents between 1989 and 2018: a meta-analysis. Int J Environ Res Public Health 16:E4581. https:\/\/doi.org\/10.3390\/ijerph16224581","journal-title":"Int J Environ Res Public Health"},{"key":"2963_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2011.08.033","volume":"92","author":"D Delgado-Gomez","year":"2012","unstructured":"Delgado-Gomez D, Blasco-Fontecilla H, Sukno F et al (2012) Suicide attempters classification: toward predictive models of suicidal behavior. Neurocomputing 92:3\u20138. https:\/\/doi.org\/10.1016\/j.neucom.2011.08.033","journal-title":"Neurocomputing"},{"key":"2963_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1037\/bul0000084","volume":"143","author":"JC Franklin","year":"2017","unstructured":"Franklin JC, Ribeiro JD, Fox KR et al (2017) Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol Bull 143:187\u2013232. https:\/\/doi.org\/10.1037\/bul0000084","journal-title":"Psychol Bull"},{"key":"2963_CR4","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/S2215-0366(22)00314-5","volume":"9","author":"MM Large","year":"2022","unstructured":"Large MM, Soper CA, Ryan CJ (2022) Suicide risk assessment. Lancet Psychiatry 9:938\u2013939. https:\/\/doi.org\/10.1016\/S2215-0366(22)00314-5","journal-title":"Lancet Psychiatry"},{"key":"2963_CR5","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1001\/jamapsychiatry.2025.2426","volume":"82","author":"J Nguyen","year":"2025","unstructured":"Nguyen J, Dwyer DB, Nelson B, Schmaal L (2025) From static to dynamic prediction of suicide risk. JAMA Psychiatry 82:1061\u20131062. https:\/\/doi.org\/10.1001\/jamapsychiatry.2025.2426","journal-title":"JAMA Psychiatry"},{"key":"2963_CR6","unstructured":"National Institute for Health and Care Excellence (2022) Self-harm: assessment, management and preventing recurrence. https:\/\/www.nice.org.uk\/guidance\/ng225\/chapter\/recommendations?utm_source=chatgpt.com. Accessed 24 Nov 2025"},{"key":"2963_CR7","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.jad.2018.11.073","volume":"245","author":"TA Burke","year":"2019","unstructured":"Burke TA, Ammerman BA, Jacobucci R (2019) The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: a systematic review. J Affect Disord 245:869\u2013884. https:\/\/doi.org\/10.1016\/j.jad.2018.11.073","journal-title":"J Affect Disord"},{"key":"2963_CR8","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1001\/jamapsychiatry.2019.0174","volume":"76","author":"BE Belsher","year":"2019","unstructured":"Belsher BE, Smolenski DJ, Pruitt LD et al (2019) Prediction models for suicide attempts and deaths: a systematic review and simulation. JAMA Psychiatry 76:642\u2013651. https:\/\/doi.org\/10.1001\/jamapsychiatry.2019.0174","journal-title":"JAMA Psychiatry"},{"key":"2963_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2023.115446","volume":"328","author":"R Su","year":"2023","unstructured":"Su R, John JR, Lin P-I (2023) Machine learning-based prediction for self-harm and suicide attempts in adolescents. Psychiatry Res 328:115446. https:\/\/doi.org\/10.1016\/j.psychres.2023.115446","journal-title":"Psychiatry Res"},{"key":"2963_CR10","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1177\/10731911231167490","volume":"31","author":"K Jankowsky","year":"2024","unstructured":"Jankowsky K, Steger D, Schroeders U (2024) Predicting lifetime suicide attempts in a community sample of adolescents using machine learning algorithms. Assessment 31:557\u2013573. https:\/\/doi.org\/10.1177\/10731911231167490","journal-title":"Assessment"},{"key":"2963_CR11","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s00127-022-02415-7","volume":"58","author":"CM McHugh","year":"2023","unstructured":"McHugh CM, Ho N, Iorfino F et al (2023) Predictive modelling of deliberate self-harm and suicide attempts in young people accessing primary care: a machine learning analysis of a longitudinal study. Soc Psychiatry Psychiatr Epidemiol 58:893\u2013905. https:\/\/doi.org\/10.1007\/s00127-022-02415-7","journal-title":"Soc Psychiatry Psychiatr Epidemiol"},{"key":"2963_CR12","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.jad.2020.02.048","volume":"268","author":"TA Burke","year":"2020","unstructured":"Burke TA, Jacobucci R, Ammerman BA et al (2020) Using machine learning to classify suicide attempt history among youth in medical care settings. J Affect Disord 268:206\u2013214. https:\/\/doi.org\/10.1016\/j.jad.2020.02.048","journal-title":"J Affect Disord"},{"key":"2963_CR13","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.jad.2019.11.093","volume":"265","author":"M Mich\u00e9","year":"2020","unstructured":"Mich\u00e9 M, Studerus E, Meyer AH et al (2020) Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning. J Affect Disord 265:570\u2013578. https:\/\/doi.org\/10.1016\/j.jad.2019.11.093","journal-title":"J Affect Disord"},{"key":"2963_CR14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211116","volume":"14","author":"NJ Carson","year":"2019","unstructured":"Carson NJ, Mullin B, Sanchez MJ et al (2019) Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records. PLoS One 14:e0211116. https:\/\/doi.org\/10.1371\/journal.pone.0211116","journal-title":"PLoS One"},{"key":"2963_CR15","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.jad.2020.10.077","volume":"280","author":"S Hong","year":"2021","unstructured":"Hong S, Liu YS, Cao B et al (2021) Identification of suicidality in adolescent major depressive disorder patients using sMRI: a machine learning approach. J Affect Disord 280:72\u201376. https:\/\/doi.org\/10.1016\/j.jad.2020.10.077","journal-title":"J Affect Disord"},{"key":"2963_CR16","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.jad.2021.06.057","volume":"294","author":"RB Penfold","year":"2021","unstructured":"Penfold RB, Johnson E, Shortreed SM et al (2021) Predicting suicide attempts and suicide deaths among adolescents following outpatient visits. J Affect Disord 294:39\u201347. https:\/\/doi.org\/10.1016\/j.jad.2021.06.057","journal-title":"J Affect Disord"},{"key":"2963_CR17","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1177\/2167702617691560","volume":"5","author":"CG Walsh","year":"2017","unstructured":"Walsh CG, Ribeiro JD, Franklin JC (2017) Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci 5:457\u2013469. https:\/\/doi.org\/10.1177\/2167702617691560","journal-title":"Clin Psychol Sci"},{"key":"2963_CR18","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1111\/jcpp.12916","volume":"59","author":"CG Walsh","year":"2018","unstructured":"Walsh CG, Ribeiro JD, Franklin JC (2018) Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. J Child Psychol Psychiatry 59:1261\u20131270. https:\/\/doi.org\/10.1111\/jcpp.12916","journal-title":"J Child Psychol Psychiatry"},{"key":"2963_CR19","doi-asserted-by":"publisher","first-page":"404","DOI":"10.13109\/prkk.2017.66.6.404","volume":"66","author":"M Kaess","year":"2017","unstructured":"Kaess M, Ghinea D, Fischer-Waldschmidt G, Resch F (2017) Die Ambulanz f\u00fcr Risikoverhalten und Selbstsch\u00e4digung (AtR!Sk) \u2013 ein Pionierkonzept der ambulanten Fr\u00fcherkennung und Fr\u00fchintervention von Borderline-Pers\u00f6nlichkeitsst\u00f6rungen. Prax Kinderpsychol Kinderpsychiatr 66:404\u2013422. https:\/\/doi.org\/10.13109\/prkk.2017.66.6.404","journal-title":"Prax Kinderpsychol Kinderpsychiatr"},{"key":"2963_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s40479-021-00161-x","volume":"8","author":"D Ghinea","year":"2021","unstructured":"Ghinea D, Fuchs A, Parzer P et al (2021) Psychosocial functioning in adolescents with non-suicidal self-injury: the roles of childhood maltreatment, borderline personality disorder and depression. Borderline Personal Disord Emot Dysregul 8:21. https:\/\/doi.org\/10.1186\/s40479-021-00161-x","journal-title":"Borderline Personal Disord Emot Dysregul"},{"key":"2963_CR21","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.1007\/s00787-023-02353-y","volume":"33","author":"Y Blaha","year":"2024","unstructured":"Blaha Y, Cavelti M, Lerch S et al (2024) Risk-taking and self-harm behaviors as markers of adolescent borderline personality disorder. Eur Child Adolesc Psychiatry 33:2743\u20132753. https:\/\/doi.org\/10.1007\/s00787-023-02353-y","journal-title":"Eur Child Adolesc Psychiatry"},{"key":"2963_CR22","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.cpr.2012.05.003","volume":"32","author":"CA Hamza","year":"2012","unstructured":"Hamza CA, Stewart SL, Willoughby T (2012) Examining the link between nonsuicidal self-injury and suicidal behavior: a review of the literature and an integrated model. Clin Psychol Rev 32:482\u2013495. https:\/\/doi.org\/10.1016\/j.cpr.2012.05.003","journal-title":"Clin Psychol Rev"},{"key":"2963_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-014-0265-0","volume":"14","author":"G Fischer","year":"2014","unstructured":"Fischer G, Ameis N, Parzer P et al (2014) The German version of the self-injurious thoughts and behaviors interview (SITBI-G): a tool to assess non-suicidal self-injury and suicidal behavior disorder. BMC Psychiatry 14:265. https:\/\/doi.org\/10.1186\/s12888-014-0265-0","journal-title":"BMC Psychiatry"},{"key":"2963_CR24","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1002\/sim.4067","volume":"30","author":"IR White","year":"2011","unstructured":"White IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: issues and guidance for practice. Stat Med 30:377\u2013399. https:\/\/doi.org\/10.1002\/sim.4067","journal-title":"Stat Med"},{"key":"2963_CR25","doi-asserted-by":"publisher","unstructured":"Buuren SV, Groothuis-Oudshoorn K (2011) Mice: multivariate imputation by chained equations in R. J Stat Softw 45. https:\/\/doi.org\/10.18637\/jss.v045.i03","DOI":"10.18637\/jss.v045.i03"},{"key":"2963_CR26","volume-title":"R: A Language and environment for statistical computing","author":"R Core Team","year":"2023","unstructured":"R Core Team (2023) R: A Language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria"},{"key":"2963_CR27","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67:301\u2013320. https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00503.x","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"2963_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"2963_CR29","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, San Francisco California USA, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"2963_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217639","volume":"14","author":"JS Jung","year":"2019","unstructured":"Jung JS, Park SJ, Kim EY et al (2019) Prediction models for high risk of suicide in Korean adolescents using machine learning techniques. PLoS One 14:e0217639. https:\/\/doi.org\/10.1371\/journal.pone.0217639","journal-title":"PLoS One"},{"key":"2963_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.actpsy.2024.104271","volume":"246","author":"J Bao","year":"2024","unstructured":"Bao J, Wan J, Li H, Sun F (2024) Psychological pain and sociodemographic factors classified suicide attempt and non-suicidal self-injury in adolescents. Acta Psychol (Amst) 246:104271. https:\/\/doi.org\/10.1016\/j.actpsy.2024.104271","journal-title":"Acta Psychol (Amst)"},{"key":"2963_CR32","doi-asserted-by":"publisher","DOI":"10.1111\/sltb.13032","author":"HK Donnelly","year":"2023","unstructured":"Donnelly HK, Richardson D, Solberg VSH (2023) Identifying important predictors of adolescent suicide ideation, planning, and attempt in low- and middle-income countries. Suicide Life Threat Behav. https:\/\/doi.org\/10.1111\/sltb.13032","journal-title":"Suicide Life Threat Behav"},{"key":"2963_CR33","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2023.1216791","volume":"14","author":"EF Haghish","year":"2023","unstructured":"Haghish EF, Czajkowski NO, von Soest T (2023) Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach. Front Psychiatry 14:1216791. https:\/\/doi.org\/10.3389\/fpsyt.2023.1216791","journal-title":"Front Psychiatry"},{"key":"2963_CR34","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S2215-0366(21)00254-6","volume":"9","author":"OJ Kirtley","year":"2022","unstructured":"Kirtley OJ, Mens Kvan, Hoogendoorn M et al (2022) Translating promise into practice: a review of machine learning in suicide research and prevention. Lancet Psychiatry 9:243\u2013252. https:\/\/doi.org\/10.1016\/S2215-0366(21)00254-6","journal-title":"Lancet Psychiatry"},{"key":"2963_CR35","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/s41380-019-0531-0","volume":"25","author":"RC Kessler","year":"2020","unstructured":"Kessler RC, Bossarte RM, Luedtke A et al (2020) Suicide prediction models: a critical review of recent research with recommendations for the way forward. Mol Psychiatry 25:168\u2013179. https:\/\/doi.org\/10.1038\/s41380-019-0531-0","journal-title":"Mol Psychiatry"},{"key":"2963_CR36","first-page":"627","volume":"4","author":"K Hajian-Tilaki","year":"2013","unstructured":"Hajian-Tilaki K (2013) Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp J Intern Med 4:627\u2013635","journal-title":"Casp J Intern Med"},{"key":"2963_CR37","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.30773\/pi.2021.0191","volume":"18","author":"K-W Kim","year":"2021","unstructured":"Kim K-W, Lim JS, Yang C-M et al (2021) Classification of adolescent psychiatric patients at high risk of suicide using the Personality Assessment Inventory by machine learning. Psychiatry Investig 18:1137\u20131143. https:\/\/doi.org\/10.30773\/pi.2021.0191","journal-title":"Psychiatry Investig"},{"key":"2963_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-020-01100-0","volume":"10","author":"C Su","year":"2020","unstructured":"Su C, Aseltine R, Doshi R et al (2020) Machine learning for suicide risk prediction in children and adolescents with electronic health records. Transl Psychiatry 10:1\u201310. https:\/\/doi.org\/10.1038\/s41398-020-01100-0","journal-title":"Transl Psychiatry"},{"key":"2963_CR39","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","volume":"110","author":"E Christodoulou","year":"2019","unstructured":"Christodoulou E, Ma J, Collins GS et al (2019) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 110:12\u201322. https:\/\/doi.org\/10.1016\/j.jclinepi.2019.02.004","journal-title":"J Clin Epidemiol"},{"key":"2963_CR40","doi-asserted-by":"crossref","unstructured":"Cleophas TJ (2024) Application of Regularized Regressions to Identify Novel Predictors in Clinical Research, 1st ed. 2024. Springer Nature Switzerland, Cham","DOI":"10.1007\/978-3-031-72247-9"},{"key":"2963_CR41","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1017\/S0033291715001804","volume":"46","author":"JD Ribeiro","year":"2016","unstructured":"Ribeiro JD, Franklin JC, Fox KR et al (2016) Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychol Med 46:225\u2013236. https:\/\/doi.org\/10.1017\/S0033291715001804","journal-title":"Psychol Med"},{"key":"2963_CR42","doi-asserted-by":"publisher","first-page":"890","DOI":"10.12688\/f1000research.19506.2","volume":"8","author":"IS Morken","year":"2019","unstructured":"Morken IS, Dahlgren A, Lunde I, Toven S (2019) The effects of interventions preventing self-harm and suicide in children and adolescents: an overview of systematic reviews. F1000Res 8:890. https:\/\/doi.org\/10.12688\/f1000research.19506.2","journal-title":"F1000Res"},{"key":"2963_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-16567-8","volume":"12","author":"LM Harris","year":"2022","unstructured":"Harris LM, Huang X, Funsch KM et al (2022) Efficacy of interventions for suicide and self-injury in children and adolescents: a meta-analysis. Sci Rep 12:12313. https:\/\/doi.org\/10.1038\/s41598-022-16567-8","journal-title":"Sci Rep"},{"key":"2963_CR44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001968","volume":"13","author":"A Gysin-Maillart","year":"2016","unstructured":"Gysin-Maillart A, Schwab S, Soravia L et al (2016) A novel brief therapy for patients who attempt suicide: a 24-months follow-up randomized controlled study of the Attempted Suicide Short Intervention Program (ASSIP). PLoS Med 13:e1001968. https:\/\/doi.org\/10.1371\/journal.pmed.1001968","journal-title":"PLoS Med"},{"key":"2963_CR45","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.jadohealth.2018.09.015","volume":"64","author":"B Kennard","year":"2019","unstructured":"Kennard B, Mayes T, King J et al (2019) The development and feasibility outcomes of a youth suicide prevention intensive outpatient program. J Adolesc Health 64:362\u2013369. https:\/\/doi.org\/10.1016\/j.jadohealth.2018.09.015","journal-title":"J Adolesc Health"},{"key":"2963_CR46","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080832","volume":"8","author":"DV Carvalho","year":"2019","unstructured":"Carvalho DV, Pereira EM, Cardoso JS (2019) Machine learning interpretability: a survey on methods and metrics. Electronics 8:832. https:\/\/doi.org\/10.3390\/electronics8080832","journal-title":"Electronics"},{"key":"2963_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpr.2020.101940","volume":"82","author":"CR Cox","year":"2020","unstructured":"Cox CR, Moscardini EH, Cohen AS, Tucker RP (2020) Machine learning for suicidology: a practical review of exploratory and hypothesis-driven approaches. Clin Psychol Rev 82:101940. https:\/\/doi.org\/10.1016\/j.cpr.2020.101940","journal-title":"Clin Psychol Rev"},{"key":"2963_CR48","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/S2215-0366(23)00036-6","volume":"10","author":"S Madigan","year":"2023","unstructured":"Madigan S, Korczak DJ, Vaillancourt T et al (2023) Comparison of paediatric emergency department visits for attempted suicide, self-harm, and suicidal ideation before and during the COVID-19 pandemic: a systematic review and meta-analysis. Lancet Psychiatry 10:342\u2013351. https:\/\/doi.org\/10.1016\/S2215-0366(23)00036-6","journal-title":"Lancet Psychiatry"},{"key":"2963_CR49","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1016\/S2215-0366(22)00232-2","volume":"9","author":"K Hawton","year":"2022","unstructured":"Hawton K, Lascelles K, Pitman A et al (2022) Assessment of suicide risk in mental health practice: shifting from prediction to therapeutic assessment, formulation, and risk management. Lancet Psychiatry 9:922\u2013928. https:\/\/doi.org\/10.1016\/S2215-0366(22)00232-2","journal-title":"Lancet Psychiatry"},{"key":"2963_CR50","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1038\/s41386-023-01724-y","volume":"49","author":"A Grzenda","year":"2024","unstructured":"Grzenda A, Widge AS (2024) Electronic health records and stratified psychiatry: bridge to precision treatment? Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 49:285\u2013290. https:\/\/doi.org\/10.1038\/s41386-023-01724-y","journal-title":"Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol"},{"key":"2963_CR51","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1002\/bsl.2392","volume":"37","author":"KP Linthicum","year":"2019","unstructured":"Linthicum KP, Schafer KM, Ribeiro JD (2019) Machine learning in suicide science: applications and ethics. Behav Sci Law 37:214\u2013222. https:\/\/doi.org\/10.1002\/bsl.2392","journal-title":"Behav Sci Law"},{"key":"2963_CR52","doi-asserted-by":"publisher","first-page":"m441","DOI":"10.1136\/bmj.m441","volume":"368","author":"RD Riley","year":"2020","unstructured":"Riley RD, Ensor J, Snell KIE et al (2020) Calculating the sample size required for developing a clinical prediction model. BMJ 368:m441. https:\/\/doi.org\/10.1136\/bmj.m441","journal-title":"BMJ"},{"key":"2963_CR53","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.876595","volume":"4","author":"L Kivel\u00e4","year":"2022","unstructured":"Kivel\u00e4 L, van der Does WAJ, Riese H, Antypa N (2022) Don\u2019t miss the moment: a systematic review of ecological momentary assessment in suicide research. Front Digit Health 4:876595. https:\/\/doi.org\/10.3389\/fdgth.2022.876595","journal-title":"Front Digit Health"},{"key":"2963_CR54","doi-asserted-by":"publisher","DOI":"10.2196\/63192","volume":"27","author":"R Melia","year":"2025","unstructured":"Melia R, Musacchio Schafer K, Rogers ML et al (2025) The application of AI to ecological momentary assessment data in suicide research: systematic review. J Med Internet Res 27:e63192. https:\/\/doi.org\/10.2196\/63192","journal-title":"J Med Internet Res"},{"key":"2963_CR55","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20021514","volume":"20","author":"A Arowosegbe","year":"2023","unstructured":"Arowosegbe A, Oyelade T (2023) Application of natural language processing (NLP) in detecting and preventing suicide ideation: a systematic review. Int J Environ Res Public Health 20:1514. https:\/\/doi.org\/10.3390\/ijerph20021514","journal-title":"Int J Environ Res Public Health"},{"key":"2963_CR56","unstructured":"Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. Data Classif Algorithms Appl 37\u201365"}],"container-title":["European Child &amp; Adolescent Psychiatry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00787-026-02963-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00787-026-02963-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00787-026-02963-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T11:20:08Z","timestamp":1781608808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00787-026-02963-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,31]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["2963"],"URL":"https:\/\/doi.org\/10.1007\/s00787-026-02963-2","relation":{},"ISSN":["1018-8827","1435-165X"],"issn-type":[{"value":"1018-8827","type":"print"},{"value":"1435-165X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,31]]},"assertion":[{"value":"13 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"MK and JK are members of the editorial board of European Child and Adolescent Psychiatry. All other authors declare no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}