{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T09:54:29Z","timestamp":1791021269086,"version":"4.1.0"},"reference-count":137,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["U01MH136059"],"award-info":[{"award-number":["U01MH136059"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biological Psychiatry: Cognitive Neuroscience and Neuroimaging"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bpsc.2026.09.012","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T15:55:23Z","timestamp":1790870123000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["EHR-based machine learning for psychiatric outcomes in youth and young adults: Challenges and opportunities"],"prefix":"10.1016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8536-2697","authenticated-orcid":false,"given":"Alysa E.","family":"Doyle","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1498-7646","authenticated-orcid":false,"given":"Anudeepa K.","family":"Ramachandiran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Bugarinovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faith","family":"Gunning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pilar F.","family":"Verhaak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2181-5220","authenticated-orcid":false,"given":"Pieter J.","family":"Vuijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Finale","family":"Doshi-Velez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy H.","family":"Perlis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bpsc.2026.09.012_bib1","unstructured":"Office of the Surgeon General. Protecting Youth Mental Health: The US Surgeon General Advisory [Internet]. 2021. Available from: https:\/\/www.hhs.gov\/sites\/default\/files\/surgeon-general-youth-mental-health-advisory.pdf"},{"issue":"6","key":"10.1016\/j.bpsc.2026.09.012_bib2","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.16612","article-title":"Factors Associated With Self-reported Symptoms of Depression Among Adults With and Without a Previous COVID-19 Diagnosis","volume":"4","author":"Perlis","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib3","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1002\/da.23203","article-title":"Gender-specificity of resilience in major depressive disorder","volume":"38","author":"Perlis","year":"2021","journal-title":"Depress Anxiety"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib4","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.3223","article-title":"Association of Acute Symptoms of COVID-19 and Symptoms of Depression in Adults","volume":"4","author":"Perlis","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"11","key":"10.1016\/j.bpsc.2026.09.012_bib5","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1001\/jamapediatrics.2021.2482","article-title":"Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19: A Meta-analysis","volume":"175","author":"Racine","year":"2021","journal-title":"JAMA Pediatr"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib6","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00787-021-01744-3","article-title":"Mostly worse, occasionally better: impact of COVID-19 pandemic on the mental health of Canadian children and adolescents","volume":"31","author":"Cost","year":"2022","journal-title":"Eur Child Adolesc Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib7","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13034-022-00441-6","article-title":"Distinct patterns of emotional and behavioral change in child psychiatry outpatients during the COVID-19 pandemic","volume":"16","author":"Doyle","year":"2022","journal-title":"Child Adolesc Psychiatry Ment Health"},{"key":"10.1016\/j.bpsc.2026.09.012_bib8","unstructured":"Youth Mental Health \u2014 Current Priorities of the U.S. Surgeon General [Internet]. [cited 2023 Mar 12]. Available from: https:\/\/www.hhs.gov\/surgeongeneral\/priorities\/youth-mental-health\/index.html"},{"key":"10.1016\/j.bpsc.2026.09.012_bib9","unstructured":"YRBSS Results | DASH | CDC [Internet]. 2023 [cited 2023 Mar 12]. Available from: https:\/\/www.cdc.gov\/healthyyouth\/data\/yrbs\/results.htm"},{"issue":"10440","key":"10.1016\/j.bpsc.2026.09.012_bib10","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1016\/S0140-6736(24)00757-8","volume":"403","year":"2024","journal-title":"Lancet"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib11","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S2215-0366(17)30167-0","article-title":"ADHD in children and young people: prevalence, care pathways, and service provision","volume":"5","author":"Sayal","year":"2018","journal-title":"Lancet Psychiatry"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib12","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1002\/aur.2696","article-title":"Global prevalence of autism: A systematic review update","volume":"15","author":"Zeidan","year":"2022","journal-title":"Autism Res"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S2215-0366(24)00363-8","article-title":"The global epidemiology and health burden of the autism spectrum: findings from the Global Burden of Disease Study 2021","volume":"12","year":"2025","journal-title":"Lancet Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.psychres.2025.116746","article-title":"Global, regional, and national trends in the burden of attention deficit hyperactivity disorder among children and adolescents (0-14 years): A 30-year analysis and future projections (1990-2036)","volume":"353","author":"Lin","year":"2025","journal-title":"Psychiatry Res"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15585\/mmwr.ss7202a1","article-title":"Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020","volume":"72","author":"Maenner","year":"2023","journal-title":"MMWR Surveill Summ"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib16","doi-asserted-by":"crossref","DOI":"10.1542\/peds.2025-070849","article-title":"The Youth Mental Health Crisis in the United States: Epidemiology, Contributors, and Potential Solutions","volume":"156","author":"Woolf","year":"2025","journal-title":"Pediatrics"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib17","doi-asserted-by":"crossref","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"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib18","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1177\/14034948221089056","article-title":"Associations between children\u2019s diagnosed mental disorders and educational achievements in Sweden","volume":"50","author":"Bortes","year":"2022","journal-title":"Scand J Public Health"},{"issue":"9","key":"10.1016\/j.bpsc.2026.09.012_bib19","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.jaac.2013.04.020","article-title":"Transitional aged youth: a new frontier in child and adolescent psychiatry","volume":"52","author":"Wilens","year":"2013","journal-title":"J Am Acad Child Adolesc Psychiatry"},{"key":"#cr-split#-10.1016\/j.bpsc.2026.09.012_bib20.1","doi-asserted-by":"crossref","unstructured":"Ahn-Horst RY, Bourgeois FT. Mental-health related outpatient visits among adolescents and young adults, 2006-2019 . JAMA Netw Open. 2024","DOI":"10.1001\/jamanetworkopen.2024.1468"},{"key":"#cr-split#-10.1016\/j.bpsc.2026.09.012_bib20.2","doi-asserted-by":"crossref","unstructured":"7(3):e241468.. PubMed PMID: 38451523. Erratum in JAMA Netw Open. 2024 Apr 1;7(4):e2410104. doi: 10.1001\/jamanetworkopen.2024.10104. PMID: 38573641","DOI":"10.1001\/jamanetworkopen.2024.10104"},{"key":"10.1016\/j.bpsc.2026.09.012_bib21","doi-asserted-by":"crossref","unstructured":"Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of general psychiatry. 2005 Jun;62(6):593\u2013602. Located at: 15939837. doi:10.1001\/archpsyc.62.6.593 PubMed PMID: 15939837.","DOI":"10.1001\/archpsyc.62.6.593"},{"issue":"7","key":"10.1016\/j.bpsc.2026.09.012_bib22","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1001\/archpsyc.60.7.709","article-title":"Prior juvenile diagnoses in adults with mental disorder: developmental follow-back of a prospective-longitudinal cohort","volume":"60","author":"Kim-Cohen","year":"2003","journal-title":"Arch Gen Psychiatry"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib23","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1001\/jamapsychiatry.2023.5051","article-title":"Worldwide Prevalence and Disability From Mental Disorders Across Childhood and Adolescence: Evidence From the Global Burden of Disease Study","volume":"81","author":"Kieling","year":"2024","journal-title":"JAMA Psychiatry"},{"issue":"9","key":"10.1016\/j.bpsc.2026.09.012_bib24","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/S2215-0366(23)00193-1","article-title":"Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries","volume":"10","author":"McGrath","year":"2023","journal-title":"Lancet Psychiatry"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib25","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1097\/00004583-200205000-00011","article-title":"Childhood-onset schizophrenia: premorbid and prodromal diagnostic and treatment histories","volume":"41","author":"Schaeffer","year":"2002","journal-title":"J Am Acad Child Adolesc Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib26","doi-asserted-by":"crossref","DOI":"10.1542\/peds.2018-3796","article-title":"Preparing Future Pediatricians to Meet the Behavioral and Mental Health Needs of Children","volume":"145","author":"McMillan","year":"2020","journal-title":"Pediatrics"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib27","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1080\/j.1440-1614.2006.01860.x","article-title":"Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions","volume":"40","author":"McGorry","year":"2006","journal-title":"Aust N Z J Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib28","doi-asserted-by":"crossref","unstructured":"Keshavan M. High risk studies, brain development and schizophrenia. In: Neurodevelopment and Schizophrenia. London: Cambridge University Press; 2004. p. 432\u2013454.","DOI":"10.1017\/CBO9780511735103.025"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib29","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1017\/S0033291711000997","article-title":"Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model","volume":"42","author":"Perlis","year":"2012","journal-title":"Psychol Med"},{"issue":"7","key":"10.1016\/j.bpsc.2026.09.012_bib30","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1111\/bdi.12556","article-title":"The International Society for Bipolar Disorders Task Force report on pediatric bipolar disorder: Knowledge to date and directions for future research","volume":"19","author":"Goldstein","year":"2017","journal-title":"Bipolar Disord"},{"key":"10.1016\/j.bpsc.2026.09.012_bib31","doi-asserted-by":"crossref","unstructured":"Biederman J, Wozniak J, Faraone SV. Pediatric mental health care dysfunction disorder? N Engl J Med. 2010 Sep 16;363(12):1187; author reply 1188-1189. doi:10.1056\/NEJMc1007166 PubMed PMID: 20843258.","DOI":"10.1056\/NEJMc1007166"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib32","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.euroneuro.2018.08.001","article-title":"Live fast, die young? A review on the developmental trajectories of ADHD across the lifespan","volume":"28","author":"Franke","year":"2018","journal-title":"Eur Neuropsychopharmacol"},{"key":"10.1016\/j.bpsc.2026.09.012_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.jad.2026.121255","article-title":"Prediction models for longitudinal trajectories of depression and anxiety: a systematic review","volume":"401","author":"Fairweather","year":"2026","journal-title":"J Affect Disord"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib34","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1177\/13623613251370818","article-title":"Latent trajectories in autistic individuals: A systematic review","volume":"30","author":"Hiralal","year":"2026","journal-title":"Autism"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib35","first-page":"93","article-title":"The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \u201cpersonalized\u201d approach to cancer staging","volume":"67","author":"Amin","year":"2017","journal-title":"CA Cancer J Clin"},{"issue":"6","key":"10.1016\/j.bpsc.2026.09.012_bib36","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/S2215-0366(19)30050-1","article-title":"Building the momentum and blueprint for reform in youth mental health","volume":"6","author":"McGorry","year":"2019","journal-title":"Lancet Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib37","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"issue":"9","key":"10.1016\/j.bpsc.2026.09.012_bib38","doi-asserted-by":"crossref","first-page":"262","DOI":"10.3758\/s13428-025-02684-5","article-title":"Automated machine learning for classification and regression: A tutorial for psychologists","volume":"57","author":"Lee","year":"2025","journal-title":"Behav Res Methods"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib39","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.biopsych.2012.12.007","article-title":"A clinical risk stratification tool for predicting treatment resistance in major depressive disorder","volume":"74","author":"Perlis","year":"2013","journal-title":"Biological psychiatry"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib40","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/s41386-020-00838-x","article-title":"Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation","volume":"46","author":"Pradier","year":"2021","journal-title":"Neuropsychopharmacology"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib41","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.5308","article-title":"Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models","volume":"3","author":"Hughes","year":"2020","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.bpsc.2026.09.012_bib42","doi-asserted-by":"crossref","unstructured":"Castro VM, Roberson AM, McCoy TH, Wiste A, Cagan A, Smoller JW, et al. Stratifying Risk for Renal Insufficiency Among Lithium-Treated Patients: An Electronic Health Record Study. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 2016 Mar;41(4):1138\u20131143. Located at: 26294109. doi:10.1038\/npp.2015.254 PubMed PMID: 26294109; PubMed Central PMCID: PMC4748438.","DOI":"10.1038\/npp.2015.254"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib43","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1001\/jamapsychiatry.2014.414","article-title":"An electronic health records study of long-term weight gain following antidepressant use","volume":"71","author":"Blumenthal","year":"2014","journal-title":"JAMA Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib44","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1186\/s12911-023-02341-x","article-title":"Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review","volume":"23","author":"Nickson","year":"2023","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.bpsc.2026.09.012_bib45","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2026.1688043","article-title":"Artificial intelligence in mental health care: a scoping review of reviews","volume":"17","author":"Abu-Mahfouz","year":"2026","journal-title":"Front Psychiatry"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib46","doi-asserted-by":"crossref","first-page":"285","DOI":"10.3390\/healthcare11030285","article-title":"A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis","volume":"11","author":"Iyortsuun","year":"2023","journal-title":"Healthcare (Basel)"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib47","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1176\/appi.ajp.2018.17101167","article-title":"Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records","volume":"175","author":"Simon","year":"2018","journal-title":"AJP"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib48","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.1428","article-title":"Prospective Validation of an Electronic Health Record\u2013Based, Real-Time Suicide Risk Model","volume":"4","author":"Walsh","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"6","key":"10.1016\/j.bpsc.2026.09.012_bib49","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1038\/s41591-022-01811-5","article-title":"Machine learning model to predict mental health crises from electronic health records","volume":"28","author":"Garriga","year":"2022","journal-title":"Nat Med"},{"key":"10.1016\/j.bpsc.2026.09.012_bib50","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jad.2024.03.162","article-title":"Identifying data-driven subtypes of major depressive disorder with electronic health records","volume":"356","author":"Sharma","year":"2024","journal-title":"Journal of Affective Disorders"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib51","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1001\/jama.2024.21700","article-title":"Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review","volume":"333","author":"Bedi","year":"2025","journal-title":"JAMA"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib52","doi-asserted-by":"crossref","first-page":"434","DOI":"10.3390\/diagnostics15040434","article-title":"Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses","volume":"15","author":"Baydili","year":"2025","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.bpsc.2026.09.012_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclinepi.2025.111675","article-title":"Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review","volume":"180","author":"Tsegaye","year":"2025","journal-title":"J Clin Epidemiol"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib54","article-title":"AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential","volume":"16","author":"Elhaddad","year":"2024","journal-title":"Cureus"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib55","doi-asserted-by":"crossref","first-page":"291","DOI":"10.2174\/1389202922666210705124359","article-title":"Machine Learning in Healthcare","volume":"22","author":"Habehh","year":"2021","journal-title":"Curr Genomics"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib56","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2026.2443","article-title":"Large Language Models Using Clinical Text in Pediatrics: A Scoping Review","volume":"9","author":"Huang","year":"2026","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.bpsc.2026.09.012_bib57","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"issue":"6","key":"10.1016\/j.bpsc.2026.09.012_bib58","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.11346","article-title":"Electronic Health Record Documentation of Psychiatric Assessments in Massachusetts Emergency Department and Outpatient Settings During the Coronavirus Disease 2019 (COVID-19) Pandemic","volume":"3","author":"Castro","year":"2020","journal-title":"JAMA Netw Open"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib59","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.3079","article-title":"Associations Between Natural Language Processing-Enriched Social Determinants of Health and Suicide Death Among US Veterans","volume":"6","author":"Mitra","year":"2023","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.bpsc.2026.09.012_bib60","doi-asserted-by":"crossref","unstructured":"Doyle AE, Bearden CE, Gur RE, Ledbetter DH, Martin CL, McCoy TH, et al. Advancing Mental Health Research Through Strategic Integration of Transdiagnostic Dimensions and Genomics. Biol Psychiatry. 2024 Oct 16;S0006-3223(24)01664-0. doi:10.1016\/j.biopsych.2024.10.006 PubMed PMID: 39424167.","DOI":"10.1016\/j.biopsych.2024.10.006"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib61","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1186\/s12916-022-02414-0","article-title":"Revisiting the seven pillars of RDoC","volume":"20","author":"Morris","year":"2022","journal-title":"BMC Med"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib62","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1002\/wps.20566","article-title":"Progress in achieving quantitative classification of psychopathology","volume":"17","author":"Krueger","year":"2018","journal-title":"World Psychiatry"},{"issue":"8096","key":"10.1016\/j.bpsc.2026.09.012_bib63","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1038\/s41586-025-09820-3","article-title":"Mapping the genetic landscape across 14 psychiatric disorders","volume":"649","author":"Grotzinger","year":"2026","journal-title":"Nature"},{"issue":"7","key":"10.1016\/j.bpsc.2026.09.012_bib64","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1176\/appi.ajp.2010.09091379","article-title":"Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders","volume":"167","author":"Insel","year":"2010","journal-title":"AJP"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib65","doi-asserted-by":"crossref","first-page":"29","DOI":"10.31887\/DCNS.2012.14.1\/smorris","article-title":"Research Domain Criteria: cognitive systems, neural circuits, and dimensions of behavior","volume":"14","author":"Morris","year":"2012","journal-title":"Dialogues in clinical neuroscience"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib66","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1017\/S0033291705004496","article-title":"Neuropsychological functioning in relatives of girls with and without ADHD","volume":"35","author":"Doyle","year":"2005","journal-title":"Psychol Med"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib67","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1017\/S0033291708004832","article-title":"Neurocognitive impairment in unaffected siblings of youth with bipolar disorder","volume":"39","author":"Doyle","year":"2009","journal-title":"Psychol Med"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib68","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1111\/jcpp.12463","article-title":"Extending the \u201ccross-disorder\u201d relevance of executive functions to dimensional neuropsychiatric traits in youth","volume":"57","author":"McGrath","year":"2016","journal-title":"J Child Psychol Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib69","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1017\/S1355617717000601","article-title":"Cross-Disorder Cognitive Impairments in Youth Referred for Neuropsychiatric Evaluation","volume":"24","author":"Doyle","year":"2018","journal-title":"J Int Neuropsychol Soc"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib70","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1093\/schbul\/sbi048","article-title":"Cognitive deficits in unaffected first-degree relatives of schizophrenia patients: a meta-analytic review of putative endophenotypes","volume":"32","author":"Snitz","year":"2006","journal-title":"Schizophr Bull"},{"key":"10.1016\/j.bpsc.2026.09.012_bib71","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.jpsychires.2022.06.003","article-title":"Evaluating cognitive function in unaffected relatives of individuals with bipolar disorders: A meta-analysis","volume":"152","author":"Gillissie","year":"2022","journal-title":"J Psychiatr Res"},{"issue":"7","key":"10.1016\/j.bpsc.2026.09.012_bib72","doi-asserted-by":"crossref","first-page":"3642","DOI":"10.3390\/ijerph18073642","article-title":"Response Inhibition, Cognitive Flexibility and Working Memory in Obsessive-Compulsive Disorder, Generalized Anxiety Disorder and Social Anxiety Disorder","volume":"18","author":"Rosa-Alc\u00e1zar","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"10.1016\/j.bpsc.2026.09.012_bib73","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbtep.2021.101674","article-title":"Cross-diagnostic cognitive heterogeneity in body dysmorphic disorder and obsessive-compulsive disorder","volume":"73","author":"Malcolm","year":"2021","journal-title":"J Behav Ther Exp Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib74","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.jad.2020.01.123","article-title":"Cognitive validation of cross-diagnostic cognitive subgroups on the schizophrenia-bipolar spectrum","volume":"266","author":"Karantonis","year":"2020","journal-title":"J Affect Disord"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib75","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1176\/ajp.2006.163.10.1730","article-title":"Impact of psychometrically defined deficits of executive functioning in adults with attention deficit hyperactivity disorder","volume":"163","author":"Biederman","year":"2006","journal-title":"Am J Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib76","doi-asserted-by":"crossref","DOI":"10.1111\/jcpp.70149","article-title":"Executive function challenges persist into young adulthood and predict mental health outcomes in autism","author":"Kenworthy","year":"2026","journal-title":"J Child Psychol Psychiatry"},{"issue":"Suppl 9","key":"10.1016\/j.bpsc.2026.09.012_bib77","first-page":"3","article-title":"Cognitive impairment and functional outcome in schizophrenia and bipolar disorder","volume":"67","author":"Green","year":"2006","journal-title":"J Clin Psychiatry"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib78","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1038\/s41380-018-0231-1","article-title":"Memory and cognition in schizophrenia","volume":"24","author":"Guo","year":"2019","journal-title":"Mol Psychiatry"},{"issue":"9","key":"10.1016\/j.bpsc.2026.09.012_bib79","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1017\/S0033291722001301","article-title":"The Hierarchical Taxonomy of Psychopathology (HiTOP) in psychiatric practice and research","volume":"52","author":"Kotov","year":"2022","journal-title":"Psychol Med"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib80","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1097\/00008480-200310000-00006","article-title":"Assessment and treatment of attention deficit hyperactivity disorder in children with comorbid psychiatric illness","volume":"15","author":"Waxmonsky","year":"2003","journal-title":"Curr Opin Pediatr"},{"key":"10.1016\/j.bpsc.2026.09.012_bib81","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2026.1765698","article-title":"Cognitive and emotional profiles in children with ASD, ADHD, and comorbid presentations: evidence for a distinct clinical phenotype","volume":"17","author":"Narzisi","year":"2026","journal-title":"Front Psychiatry"},{"issue":"7","key":"10.1016\/j.bpsc.2026.09.012_bib82","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1037\/amp0001046","article-title":"Translating the Hierarchical Taxonomy of Psychopathology (HiTOP) from Potential to Practice: Ten Research Questions","volume":"78","author":"Conway","year":"2023","journal-title":"Am Psychol"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib83","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1089\/cap.2015.0126","article-title":"Impulsive Aggression as a Comorbidity of Attention-Deficit\/Hyperactivity Disorder in Children and Adolescents","volume":"26","author":"Saylor","year":"2016","journal-title":"J Child Adolesc Psychopharmacol"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib84","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1586\/ern.10.146","article-title":"Aggression in children with attention-deficit\/hyperactivity disorder","volume":"10","author":"King","year":"2010","journal-title":"Expert Rev Neurother"},{"key":"10.1016\/j.bpsc.2026.09.012_bib85","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.psychres.2017.02.029","article-title":"Mortality in individuals with disruptive behavior disorders diagnosed by specialist services - A nationwide cohort study","volume":"251","author":"Scott","year":"2017","journal-title":"Psychiatry Res"},{"issue":"9","key":"10.1016\/j.bpsc.2026.09.012_bib86","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1001\/jamapsychiatry.2014.359","article-title":"Patterns of heterotypic continuity associated with the cross-sectional correlational structure of prevalent mental disorders in adults","volume":"71","author":"Lahey","year":"2014","journal-title":"JAMA Psychiatry"},{"issue":"12","key":"10.1016\/j.bpsc.2026.09.012_bib87","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1017\/S003329171700054X","article-title":"Examining heterotypic continuity of psychopathology: a prospective national study","volume":"47","author":"Blanco","year":"2017","journal-title":"Psychol Med"},{"key":"10.1016\/j.bpsc.2026.09.012_bib88","doi-asserted-by":"crossref","first-page":"e26","DOI":"10.1017\/S003329172400223X","article-title":"New diagnosis in psychiatry: beyond heuristics","volume":"55","author":"McGorry","year":"2025","journal-title":"Psychol Med"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib89","doi-asserted-by":"crossref","first-page":"282","DOI":"10.3390\/healthcare14020282","article-title":"A Systematic Review of Topic Modeling Techniques for Electronic Health Records","volume":"14","author":"Mehmood","year":"2026","journal-title":"Healthcare (Basel)"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib90","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/hcs2.61","article-title":"Large language models in health care: Development, applications, and challenges","volume":"2","author":"Yang","year":"2023","journal-title":"Health Care Sci"},{"key":"10.1016\/j.bpsc.2026.09.012_bib91","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.genhosppsych.2020.11.003","article-title":"Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method","volume":"68","author":"Hart","year":"2021","journal-title":"Gen Hosp Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib92","first-page":"1","article-title":"Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records","volume":"23","author":"Barroilhet","year":"2019","journal-title":"Psychol Med"},{"key":"10.1016\/j.bpsc.2026.09.012_bib93","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.genhosppsych.2020.01.003","article-title":"Association between DSM-5 and ICD-11 personality dimensional traits in a general medical cohort and readmission and mortality","volume":"64","author":"Barroilhet","year":"2020","journal-title":"General Hospital Psychiatry"},{"issue":"12","key":"10.1016\/j.bpsc.2026.09.012_bib94","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.biopsych.2018.01.011","article-title":"High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records","volume":"83","author":"McCoy","year":"2018","journal-title":"Biol Psychiatry"},{"issue":"12","key":"10.1016\/j.bpsc.2026.09.012_bib95","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.biopsych.2017.12.004","article-title":"Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records","volume":"83","author":"McCoy","year":"2018","journal-title":"Biol Psychiatry"},{"issue":"381","key":"10.1016\/j.bpsc.2026.09.012_bib96","first-page":"270","article-title":"Estimating depression severity in narrative clinical notes using large language models","volume":"15","author":"McCoy","year":"2024","journal-title":"J Affect Disord"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib97","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1001\/jamapsychiatry.2016.2172","article-title":"Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing","volume":"73","author":"McCoy","year":"2016","journal-title":"JAMA psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib98","doi-asserted-by":"crossref","DOI":"10.1093\/jamiaopen\/ooab011","article-title":"Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts","volume":"4","author":"Tsui","year":"2021","journal-title":"JAMIA Open"},{"issue":"15","key":"10.1016\/j.bpsc.2026.09.012_bib99","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1017\/S0033291724002642","article-title":"Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data","volume":"54","author":"Perfalk","year":"2024","journal-title":"Psychol Med"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib100","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.jalz.2019.09.084","article-title":"Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study","volume":"16","author":"McCoy","year":"2020","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"10.1016\/j.bpsc.2026.09.012_bib101","article-title":"QT interval and antidepressant use: a cross sectional study of electronic health records","volume":"346","author":"Castro","year":"2013","journal-title":"BMJ (Clinical research ed)"},{"issue":"6","key":"10.1016\/j.bpsc.2026.09.012_bib102","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1097\/DBP.0000000000000188","article-title":"A Systematic Evaluation of the QTc Interval and Antidepressants in Youth: An Electronic Health Record Study","volume":"36","author":"Uchida","year":"2015","journal-title":"Journal of developmental and behavioral pediatrics: JDBP"},{"issue":"12","key":"10.1016\/j.bpsc.2026.09.012_bib103","doi-asserted-by":"crossref","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":"Journal of Child Psychology and Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib104","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.jad.2021.06.057","article-title":"Predicting suicide attempts and suicide deaths among adolescents following outpatient visits","volume":"294","author":"Penfold","year":"2021","journal-title":"Journal of Affective Disorders"},{"key":"10.1016\/j.bpsc.2026.09.012_bib105","doi-asserted-by":"crossref","unstructured":"McCoy TH, Perlis RH. Dimensional Measures of Psychopathology in Children and Adolescents Using Large Language Models. Biol Psychiatry. 2024 Jun 10;S0006-3223(24)01299-X. doi:10.1016\/j.biopsych.2024.05.008 PubMed PMID: 38866172.","DOI":"10.1016\/j.biopsych.2024.05.008"},{"key":"10.1016\/j.bpsc.2026.09.012_bib106","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.genhosppsych.2019.04.009","article-title":"Association between child psychiatric emergency room outcomes and dimensions of psychopathology","volume":"59","author":"McCoy","year":"2019","journal-title":"Gen Hosp Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib107","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.jad.2024.08.043","article-title":"Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence","volume":"365","author":"Kusuma","year":"2024","journal-title":"J Affect Disord"},{"key":"10.1016\/j.bpsc.2026.09.012_bib108","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109859","article-title":"Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review","volume":"188","author":"Ibrahim","year":"2025","journal-title":"Comput Biol Med"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib109","article-title":"Machine Learning Models for Predicting Mental Health Crises in Adolescents Using Electronic Health Records: A Systematic Review","volume":"17","author":"Al-Juhani","year":"2025","journal-title":"Cureus"},{"issue":"Suppl 1","key":"10.1016\/j.bpsc.2026.09.012_bib110","first-page":"79","article-title":"Outcimes in machine learning models for child psychiatry: A systematic review of the literature","volume":"37","author":"Till","year":"2025","journal-title":"Psychiatr Danub"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib111","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/jcpp.13545","article-title":"Annual Research Review: Translational machine learning for child and adolescent psychiatry","volume":"63","author":"Dwyer","year":"2022","journal-title":"Child Psychology Psychiatry"},{"issue":"10","key":"10.1016\/j.bpsc.2026.09.012_bib112","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2024.42091","article-title":"Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits","volume":"7","author":"Edgcomb","year":"2024","journal-title":"JAMA Netw Open"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib113","doi-asserted-by":"crossref","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"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib114","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1111\/sltb.12988","article-title":"Using electronic health record data to predict future self-harm or suicidal ideation in young people treated by child and youth mental health services","volume":"53","author":"Tennakoon","year":"2023","journal-title":"Suicide Life Threat Behav"},{"issue":"9","key":"10.1016\/j.bpsc.2026.09.012_bib115","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2025.33505","article-title":"Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department","volume":"8","author":"Aseltine","year":"2025","journal-title":"JAMA Netw Open"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib116","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/s41398-024-03034-3","article-title":"Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records","volume":"14","author":"Zang","year":"2024","journal-title":"Transl Psychiatry"},{"key":"10.1016\/j.bpsc.2026.09.012_bib117","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.jad.2023.12.059","article-title":"Predicting Adolescent Suicidal Behavior Following Inpatient Discharge Using Structured and Unstructured Data","volume":"350","author":"Carson","year":"2024","journal-title":"J Affect Disord"},{"key":"10.1016\/j.bpsc.2026.09.012_bib118","doi-asserted-by":"crossref","DOI":"10.2196\/82371","article-title":"Detecting Pediatric Emergency Service Use for Suicide and Self-Harm: Multimodal Analysis of 3828 Encounters","volume":"13","author":"Edgcomb","year":"2026","journal-title":"JMIR Ment Health"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib119","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1093\/schbul\/sbaf064","article-title":"Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data","volume":"51","author":"Visoki","year":"2025","journal-title":"Schizophr Bull"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib120","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1186\/s13034-023-00650-7","article-title":"Electronic health records identify timely trends in childhood mental health conditions","volume":"17","author":"Elia","year":"2023","journal-title":"Child and Adolescent Psychiatry and Mental Health"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib121","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jaac.2024.03.023","article-title":"Post-COVID-19 Mental Health Distress in 13 Million Youth: A Retrospective Cohort Study of Electronic Health Records","volume":"64","author":"Zhang-James","year":"2025","journal-title":"J Am Acad Child Adolesc Psychiatry"},{"issue":"4","key":"10.1016\/j.bpsc.2026.09.012_bib122","first-page":"429","article-title":"A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning","volume":"11","author":"Ben-Sasson","year":"2024","journal-title":"Children (Basel)"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib123","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1002\/aur.2912","article-title":"Development and validation of a machine learning-based tool to predict autism among children","volume":"16","author":"Betts","year":"2023","journal-title":"Autism Research"},{"issue":"2","key":"10.1016\/j.bpsc.2026.09.012_bib124","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.54303","article-title":"Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year","volume":"6","author":"Engelhard","year":"2023","journal-title":"JAMA Netw Open"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib125","article-title":"Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data","volume":"15","author":"Dick","year":"2025","journal-title":"Sci Rep"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib126","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1038\/s41380-022-01918-8","article-title":"Predicting childhood and adolescent attention-deficit\/hyperactivity disorder onset: a nationwide deep learning approach","volume":"28","author":"Garcia-Argibay","year":"2023","journal-title":"Mol Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib127","article-title":"Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation","volume":"12","author":"Mardini","year":"2025","journal-title":"JMIR Mental Health"},{"issue":"8","key":"10.1016\/j.bpsc.2026.09.012_bib128","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1111\/jcpp.14131","article-title":"Prediction of early-onset bipolar using electronic health records","volume":"66","author":"Wang","year":"2025","journal-title":"Journal of Child Psychology and Psychiatry"},{"issue":"12","key":"10.1016\/j.bpsc.2026.09.012_bib129","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1111\/jcpp.13226","article-title":"Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data","volume":"61","author":"Zhang-James","year":"2020","journal-title":"Journal of Child Psychology and Psychiatry"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib130","doi-asserted-by":"crossref","DOI":"10.1192\/j.eurpsy.2025.10095","article-title":"Mood instability as a transdiagnostic predictor of cannabis use in attention-deficit\/hyperactivity disorder and depression: A natural language processing analysis of electronic health records from 13,025 adolescents","volume":"68","author":"Seker","year":"2025","journal-title":"Eur Psychiatry"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib131","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2014-007504","article-title":"Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes","volume":"5","author":"Patel","year":"2015","journal-title":"BMJ Open"},{"issue":"3","key":"10.1016\/j.bpsc.2026.09.012_bib132","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.jaacop.2024.09.001","article-title":"Electronic Health Record-Integrated Legal Documentation to Measure Involuntary Mental Health Detention of Children","volume":"3","author":"Edgcomb","year":"2025","journal-title":"JAACAP Open"},{"key":"10.1016\/j.bpsc.2026.09.012_bib133","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s12911-024-02450-1","article-title":"Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques","volume":"24","author":"Saggu","year":"2024","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"10.1016\/j.bpsc.2026.09.012_bib134","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12911-025-03269-0","article-title":"Predicting child and adolescent mental health emergency department revisits: a machine-learning approach compared to a clinician-derived baseline","volume":"26","author":"Bians","year":"2025","journal-title":"BMC Med Inform Decis Mak"},{"issue":"6","key":"10.1016\/j.bpsc.2026.09.012_bib135","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1038\/s41380-022-01528-4","article-title":"Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges","volume":"27","author":"Meehan","year":"2022","journal-title":"Mol Psychiatry"},{"issue":"5","key":"10.1016\/j.bpsc.2026.09.012_bib136","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.neuron.2019.02.024","article-title":"Advancing Translational Research Using NIMH Research Domain Criteria and Computational Methods","volume":"101","author":"Sanislow","year":"2019","journal-title":"Neuron"}],"container-title":["Biological Psychiatry: Cognitive Neuroscience and Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2451902226002831?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2451902226002831?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T09:07:58Z","timestamp":1791018478000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2451902226002831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":137,"alternative-id":["S2451902226002831"],"URL":"https:\/\/doi.org\/10.1016\/j.bpsc.2026.09.012","relation":{},"ISSN":["2451-9022"],"issn-type":[{"value":"2451-9022","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EHR-based machine learning for psychiatric outcomes in youth and young adults: Challenges and opportunities","name":"articletitle","label":"Article Title"},{"value":"Biological Psychiatry: Cognitive Neuroscience and Neuroimaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bpsc.2026.09.012","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc on behalf of Society of Biological Psychiatry.","name":"copyright","label":"Copyright"}]}}