{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:44:05Z","timestamp":1785545045460,"version":"3.56.0"},"reference-count":59,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Ment Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting of preventive interventions. While prognostic models have primarily focused on psychosis, early recognition services require a transdiagnostic approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to predict global functioning within a 2-year follow-up using baseline clinical and structural magnetic resonance imaging (MRI) data in a population-based sample of young, help-seeking individuals presenting with affective and anxiety symptoms as well as attention-deficit hyperactivity disorder.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We classified 357 help-seeking individuals aged 18\u201035 years recruited from 9 sites as \u201cimpaired\u201d (Global Assessment of Functioning [GAF] \u226460; n=228) or \u201cnonimpaired\u201d (GAF&gt;60; n=129) at year 1 and\/or year 2 follow-up. GAF classification group status at follow-up was predicted using linear support vector machine (SVM), decision tree, and large language model (LLM) Llama-3 using clinical assessments and\/or structural MRI. Leave-one-site-out (SVM) or external sample (LLM) was used for validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>SVM achieved balanced accuracy of 69.2% using clinical features only. Items related to baseline occupational functioning, interpersonal relationships, cognitive functioning, psychotic and affective symptoms, as well as the presence of anxiety disorder, were most predictive. The decision tree further reduced the feature set to 5 predictive items, achieving balanced accuracy of 76.6%. Although amygdala and hippocampal subregions achieved balanced accuracy of 57.1%, structural MRI did not improve the overall prediction. Llama-3 performed comparably well to SVM (balanced accuracy of 72.6%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning demonstrated good performance in predicting global functioning. Interestingly, the out-of-the-box LLM performed comparably well without being trained or fine-tuned, highlighting the potential of leveraging free-text data for mental health prognosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/84424","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T14:55:09Z","timestamp":1765205709000},"page":"e84424-e84424","source":"Crossref","is-referenced-by-count":0,"title":["Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3974-7115","authenticated-orcid":false,"given":"Pavol","family":"Mikolas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1240-2421","authenticated-orcid":false,"given":"Fabian","family":"Huth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6490-3076","authenticated-orcid":false,"given":"Kyra","family":"Br\u00f6ckel-Bundt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0191-7609","authenticated-orcid":false,"given":"Christina","family":"Berndt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9524-8257","authenticated-orcid":false,"given":"Julia","family":"Martini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0374-342X","authenticated-orcid":false,"given":"Birgit","family":"Maicher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8870-0041","authenticated-orcid":false,"given":"Michael","family":"Marxen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6808-2968","authenticated-orcid":false,"given":"Falk Gerrik","family":"Verhees","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6684-8183","authenticated-orcid":false,"given":"Paula Marie","family":"Henneberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3191-1400","authenticated-orcid":false,"given":"Christoph","family":"Vogelbacher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3140-8207","authenticated-orcid":false,"given":"Andreas","family":"Jansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2514-2625","authenticated-orcid":false,"given":"Tilo","family":"Kircher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3699-0803","authenticated-orcid":false,"given":"Irina","family":"Falkenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4912-8307","authenticated-orcid":false,"given":"Florian","family":"Thomas-Odenthal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-3895","authenticated-orcid":false,"given":"Martin","family":"Lambert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5815-1026","authenticated-orcid":false,"given":"Vivien","family":"Kraft","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5104-9336","authenticated-orcid":false,"given":"Gregor","family":"Leicht","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8214-5868","authenticated-orcid":false,"given":"Christoph","family":"Mulert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7713-4259","authenticated-orcid":false,"given":"Andreas J","family":"Fallgatter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4612-6018","authenticated-orcid":false,"given":"Thomas","family":"Ethofer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9187-144X","authenticated-orcid":false,"given":"Anne","family":"Rau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3445-1611","authenticated-orcid":false,"given":"Karolina","family":"Leopold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4796-3392","authenticated-orcid":false,"given":"Andreas","family":"Bechdolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0992-634X","authenticated-orcid":false,"given":"Reif","family":"Andreas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7666-9534","authenticated-orcid":false,"given":"Silke","family":"Matura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1379-9491","authenticated-orcid":false,"given":"Jonathan","family":"Repple","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3260-6328","authenticated-orcid":false,"given":"Felix","family":"Bermpohl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0753-0504","authenticated-orcid":false,"given":"Jana","family":"Fiebig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8557-629X","authenticated-orcid":false,"given":"Thomas","family":"Stamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-5646","authenticated-orcid":false,"given":"Christoph U","family":"Correll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9860-9620","authenticated-orcid":false,"given":"Georg","family":"Juckel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6405-4567","authenticated-orcid":false,"given":"Vera","family":"Flasbeck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4848-614X","authenticated-orcid":false,"given":"Isabella C","family":"Wiest","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3730-5348","authenticated-orcid":false,"given":"Jakob N","family":"Kather","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0623-3759","authenticated-orcid":false,"given":"Udo","family":"Dannlowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5099-0274","authenticated-orcid":false,"given":"Eva","family":"Mennigen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4286-5830","authenticated-orcid":false,"given":"Philipp","family":"Ritter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1521-3514","authenticated-orcid":false,"given":"Michael","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3415-5583","authenticated-orcid":false,"given":"Andrea","family":"Pfennig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"R1","unstructured":"Pension insurance in time series. German Pension Insurance. 2024. URL: https:\/\/www.deutsche-rentenversicherung.de\/SharedDocs\/Downloads\/DE\/Statistiken-und-Berichte\/statistikpublikationen\/rv_in_zeitreihen.html [Accessed 27-11-2024]"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101675","article-title":"Quantifying the global burden of mental disorders and their economic value","volume":"54","author":"Arias","journal-title":"EClinicalMedicine"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2023.115667","article-title":"Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment","volume":"333","author":"Kjell","journal-title":"Psychiatry Res"},{"issue":"10","key":"R4","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large language model influence on diagnostic reasoning: a randomized clinical trial","volume":"7","author":"Goh","journal-title":"JAMA Netw Open"},{"issue":"3","key":"R5","doi-asserted-by":"publisher","DOI":"10.1001\/jamahealthforum.2025.0040","article-title":"Comparison of frontier open-source and proprietary large language models for complex diagnoses","volume":"6","author":"Buckley","journal-title":"JAMA Health Forum"},{"issue":"6","key":"R6","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1192\/bjp.2024.134","article-title":"Detection of suicidality from medical text using privacy-preserving large language models","volume":"225","author":"Wiest","journal-title":"Br J Psychiatry"},{"issue":"12","key":"R7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000680","article-title":"CPLLM: clinical prediction with large language models","volume":"3","author":"Ben Shoham","journal-title":"PLOS Digit Health"},{"issue":"5","key":"R8","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1093\/jamia\/ocaf038","article-title":"Large language models are less effective at clinical prediction tasks than locally trained machine learning models","volume":"32","author":"Brown","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","first-page":"7857","DOI":"10.1038\/s41467-023-43715-z","article-title":"TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records","volume":"14","author":"Yang","journal-title":"Nat Commun"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","DOI":"10.1186\/1744-859X-10-2","article-title":"Guidelines for rating Global Assessment of Functioning (GAF)","volume":"10","author":"Aas","journal-title":"Ann Gen Psychiatry"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1093\/schbul\/sbad062","article-title":"Measures of social and occupational function in early psychosis: a systematic review and meta-analysis","volume":"50","author":"Cowman","journal-title":"Schizophr Bull"},{"issue":"11","key":"R12","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1001\/jamapsychiatry.2018.2165","article-title":"Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis","volume":"75","author":"Koutsouleris","journal-title":"JAMA Psychiatry"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.1038\/s41537-024-00505-w","article-title":"Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN","volume":"10","author":"Slot","journal-title":"Schizophrenia (Heidelb)"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","DOI":"10.1038\/s41537-021-00162-3","article-title":"Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach","volume":"7","author":"de Nijs","journal-title":"NPJ Schizophr"},{"issue":"6","key":"R15","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1017\/S0033291721004153","article-title":"Longitudinal clinical and functional outcome in distinct cognitive subgroups of first-episode psychosis: a cluster analysis","volume":"53","author":"Oomen","journal-title":"Psychol Med"},{"issue":"11","key":"R16","doi-asserted-by":"publisher","first-page":"3305","DOI":"10.1038\/s41380-024-02533-5","article-title":"A transdiagnostic prodrome for severe mental disorders: an electronic health record study","volume":"29","author":"Arribas","journal-title":"Mol Psychiatry"},{"issue":"1","key":"R17","doi-asserted-by":"publisher","DOI":"10.1186\/s40345-020-00183-4","article-title":"Improving early recognition and intervention in people at increased risk for the development of bipolar disorder: study protocol of a prospective-longitudinal, naturalistic cohort study (Early-BipoLife)","volume":"8","author":"Pfennig","journal-title":"Int J Bipolar Disord"},{"issue":"3","key":"R18","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.jad.2011.06.043","article-title":"Risk constellations prior to the development of bipolar disorders: rationale of a new risk assessment tool","volume":"136","author":"Leopold","journal-title":"J Affect Disord"},{"key":"R19","doi-asserted-by":"publisher","DOI":"10.1016\/j.euroneuro.2023.10.001","article-title":"Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study","volume":"78","author":"Martini","journal-title":"Eur Neuropsychopharmacol"},{"key":"R20","unstructured":"Wittchen HU Zaudig M Fydrich T SKIDet al. Strukturiertes klinisches interview f\u00fcr DSM-IV. In: Achse I Und II: Achse I: Psychische St\u00f6rungen; Achse II: Pers\u00f6nlichkeitst\u00f6rungen 1 Auflage. G\u00f6ttingen: Hogrefe; 1997."},{"issue":"1","key":"R21","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/0165-1781(86)90060-0","article-title":"The Inventory for Depressive Symptomatology (IDS): preliminary findings","volume":"18","author":"Rush","journal-title":"Psychiatry Res"},{"key":"R22","doi-asserted-by":"crossref","unstructured":"Bernstein DP Fink L Handelsman L Foote J . Childhood trauma questionnaire. APA PsycNet. 2011. URL: https:\/\/doi.org\/10.1037\/t02080-000 [Accessed 26-03-2026]","DOI":"10.1037\/t02080-000"},{"issue":"1","key":"R23","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/1745-0179-3-5","article-title":"Validity and reliability of the Functioning Assessment Short Test (FAST) in bipolar disorder","volume":"3","author":"Rosa","journal-title":"Clin Pract Epidemiol Ment Health"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2024.07.018","article-title":"The Functioning Assessment Short Test (FAST): clinically meaningful response threshold in patients with major depressive disorder receiving antidepressant treatment","volume":"363","author":"Christensen","journal-title":"J Affect Disord"},{"issue":"6","key":"R25","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1093\/schbul\/sbs068","article-title":"The validity of the 16-item version of the Prodromal Questionnaire (PQ-16) to screen for ultra high risk of developing psychosis in the general help-seeking population","volume":"38","author":"Ising","journal-title":"Schizophr Bull"},{"issue":"5","key":"R26","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1111\/bdi.12205","article-title":"The predictive validity of bipolar at-risk (prodromal) criteria in help-seeking adolescents and young adults: a prospective study","volume":"16","author":"Bechdolf","journal-title":"Bipolar Disord"},{"issue":"5","key":"R27","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1111\/bdi.12209","article-title":"The Bipolar Prodrome Symptom Interview and Scale-Prospective (BPSS-P): description and validation in a psychiatric sample and healthy controls","volume":"16","author":"Correll","journal-title":"Bipolar Disord"},{"issue":"7","key":"R28","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.biopsych.2022.03.021","article-title":"Neurobiologically based stratification of recent-onset depression and psychosis: identification of two distinct transdiagnostic phenotypes","volume":"92","author":"Lalousis","journal-title":"Biol Psychiatry"},{"key":"R29","unstructured":"Scikit-learn: machine learning in python. scikit-learn. URL: https:\/\/scikit-learn.org\/stable\/ [Accessed 28-04-2026]"},{"key":"R30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.01.079","article-title":"The Marburg-M\u00fcnster Affective Disorders Cohort Study (MACS): a quality assurance protocol for MR neuroimaging data","volume":"172","author":"Vogelbacher","journal-title":"Neuroimage"},{"issue":"9","key":"R31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0184661","article-title":"MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites","volume":"12","author":"Esteban","journal-title":"PLoS One"},{"key":"R32","unstructured":"Data intensive computing \u2013 high performance computing. Dresden University of Technology. URL: https:\/\/tu-dresden.de\/zih\/hochleistungsrechnen [Accessed 26-03-2026]"},{"issue":"3","key":"R33","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","journal-title":"Neuroimage"},{"issue":"1","key":"R34","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1093\/cercor\/bhg087","article-title":"Automatically parcellating the human cerebral cortex","volume":"14","author":"Fischl","journal-title":"Cereb Cortex"},{"key":"R35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.04.042","article-title":"A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI","volume":"115","author":"Iglesias","journal-title":"Neuroimage"},{"issue":"542\u2013555","key":"R36","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.neuroimage.2016.07.020","article-title":"Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases","volume":"141","author":"Iglesias","journal-title":"Neuroimage"},{"key":"R37","unstructured":"Structural image processing protocols. ENIGMA. URL: http:\/\/enigma.ini.usc.edu\/protocols\/imaging-protocols [Accessed 26-03-2026]"},{"issue":"2","key":"R38","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1017\/S0033291723001319","article-title":"Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features","volume":"54","author":"Mikolas","journal-title":"Psychol Med"},{"issue":"6","key":"R39","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13060870","article-title":"Machine learning prediction of estimated risk for bipolar disorders using hippocampal subfield and amygdala nuclei volumes","volume":"13","author":"Huth","journal-title":"Brain Sci"},{"key":"R40","unstructured":"LIBSVM: a library for support vector machines. ACM Digital Library. URL: https:\/\/dl.acm.org\/doi\/10.1145\/1961189.1961199 [Accessed 28-04-2026]"},{"key":"R41","unstructured":"Neurominer-git\/neurominer_1.2. GitHub. URL: https:\/\/github.com\/neurominer-git\/NeuroMiner_1.2 [Accessed 26-03-2026]"},{"issue":"2","key":"R42","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1001\/jamapsychiatry.2020.3604","article-title":"Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression","volume":"78","author":"Koutsouleris","journal-title":"JAMA Psychiatry"},{"issue":"4","key":"R43","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1001\/jama.2015.37","article-title":"Decision curve analysis","volume":"313","author":"Fitzgerald","journal-title":"JAMA"},{"issue":"8","key":"R44","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s00406-018-0943-x","article-title":"Neurobiology of the major psychoses: a translational perspective on brain structure and function-the FOR2107 consortium","volume":"269","author":"Kircher","journal-title":"Eur Arch Psychiatry Clin Neurosci"},{"issue":"1","key":"R45","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1002\/hbm.25098","article-title":"What we learn about bipolar disorder from large-scale neuroimaging: findings and future directions from the ENIGMA Bipolar Disorder Working Group","volume":"43","author":"Ching","journal-title":"Hum Brain Mapp"},{"issue":"2","key":"R46","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.bpsgos.2021.09.002","article-title":"Structural brain volumes of individuals at clinical high risk for psychosis: a meta-analysis","volume":"2","author":"Vissink","journal-title":"Biol Psychiatry Glob Open Sci"},{"issue":"6","key":"R47","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1038\/mp.2016.60","article-title":"Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group","volume":"22","author":"Schmaal","journal-title":"Mol Psychiatry"},{"issue":"12","key":"R48","doi-asserted-by":"publisher","first-page":"1710","DOI":"10.1038\/mp.2015.227","article-title":"Subcortical volumetric abnormalities in bipolar disorder","volume":"21","author":"Hibar","journal-title":"Mol Psychiatry"},{"issue":"3","key":"R49","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1002\/hbm.25263","article-title":"Emotion processing and regulation in major depressive disorder: a 7T resting-state fMRI study","volume":"42","author":"Ebneabbasi","journal-title":"Hum Brain Mapp"},{"key":"R50","doi-asserted-by":"crossref","unstructured":"Verhees FG Huth F Meyer V et al. clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical Psychiatry. Cold Spring Harbor Laboratory; 2025. [doi: 10.1101\/2025.06.28.25330267]","DOI":"10.1101\/2025.06.28.25330267"},{"issue":"1","key":"R51","doi-asserted-by":"publisher","first-page":"e12","DOI":"10.1016\/S2589-7500(23)00225-X","article-title":"Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study","volume":"6","author":"Zack","journal-title":"Lancet Digit Health"},{"issue":"1","key":"R52","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s41746-024-01366-4","article-title":"Probabilistic medical predictions of large language models","volume":"7","author":"Gu","journal-title":"NPJ Digit Med"},{"key":"R53","doi-asserted-by":"crossref","unstructured":"Rybarczyk B . Social and occupational functioning assessment scale (SOFAS). In: Kreutzer JS DeLuca J Caplan B , editors. Encyclopedia of Clinical Neuropsychology. Springer; 2011:2313-2313. [doi: 10.1007\/978-0-387-79948-3_428] ISBN: 978-0-387-79947-6","DOI":"10.1007\/978-0-387-79948-3_428"},{"issue":"12","key":"R54","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1038\/mp.2010.101","article-title":"Days out of role due to common physical and mental conditions: results from the WHO World Mental Health surveys","volume":"16","author":"Alonso","journal-title":"Mol Psychiatry"},{"issue":"2-3","key":"R55","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.schres.2014.12.011","article-title":"Comparing time use in individuals at different stages of psychosis and a non-clinical comparison group","volume":"161","author":"Hodgekins","journal-title":"Schizophr Res"},{"issue":"9","key":"R56","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.1038\/s41380-018-0228-9","article-title":"Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group","volume":"25","author":"Nunes","journal-title":"Mol Psychiatry"},{"key":"R57","doi-asserted-by":"publisher","DOI":"10.1016\/j.euroneuro.2021.05.006","article-title":"Route map for machine learning in psychiatry: absence of bias, reproducibility, and utility","volume":"50","author":"Radua","journal-title":"Eur Neuropsychopharmacol"},{"issue":"2","key":"R58","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1093\/schbul\/sbaa126","article-title":"Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk","volume":"47","author":"Irving","journal-title":"Schizophr Bull"},{"key":"R59","unstructured":"Pmiko\/bl_gaf. GitHub. URL: https:\/\/github.com\/pmiko\/BL_GAF [Accessed 26-03-2026]"}],"container-title":["JMIR Mental Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/mental.jmir.org\/2026\/1\/e84424","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:26:01Z","timestamp":1785536761000},"score":1,"resource":{"primary":{"URL":"https:\/\/mental.jmir.org\/2026\/1\/e84424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":59,"URL":"https:\/\/doi.org\/10.2196\/84424","relation":{},"ISSN":["2368-7959"],"issn-type":[{"value":"2368-7959","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]},"article-number":"v13i10e84424"}}