{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T07:17:39Z","timestamp":1789888659395,"version":"4.0.1"},"reference-count":130,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Background: Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner. Objective: In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes. Methods: This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024. Results: Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions. Conclusions: The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders.<\/jats:p>","DOI":"10.3390\/brainsci14090878","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T09:39:32Z","timestamp":1725010772000},"page":"878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9050-6520","authenticated-orcid":false,"given":"Jing Ling","family":"Tay","sequence":"first","affiliation":[{"name":"West Region, Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyawt Kyawt","family":"Htun","sequence":"additional","affiliation":[{"name":"Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3209-9626","authenticated-orcid":false,"given":"Kang","family":"Sim","sequence":"additional","affiliation":[{"name":"West Region, Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore"},{"name":"Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore"},{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Clinical Sciences, Building, 11 Mandalay Road, Level 18, Singapore 308232, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1001\/archpsyc.64.1.19","article-title":"Lifetime prevalence of psychotic and bipolar I disorders in a general population","volume":"64","author":"Suvisaari","year":"2007","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association.","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Casey, P., Casey, P.R., and Kelly, B. (2019). Fish\u2019s Clinical Psychopathology: Signs and Symptoms in Psychiatry, Cambridge University Press.","DOI":"10.1017\/9781108578264"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/wps.20693","article-title":"Dopamine and glutamate in schizophrenia: Biology, symptoms and treatment","volume":"19","author":"McCutcheon","year":"2020","journal-title":"World Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1038\/nm1478","article-title":"Pathophysiologically based treatment interventions in schizophrenia","volume":"12","author":"Lewis","year":"2006","journal-title":"Nat. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"GBD Mental Disorders Collaborators (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry, 9, 137\u2013150.","DOI":"10.1016\/S2215-0366(21)00395-3"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1017\/ipm.2022.3","article-title":"Premature mortality of people with severe mental illness: A renewed focus for a new era","volume":"40","author":"Byrne","year":"2023","journal-title":"Ir. J. Psychol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1080\/08039488.2020.1745273","article-title":"Different response patterns in hallucinations and delusions to antipsychotic treatment","volume":"74","author":"Bjarke","year":"2020","journal-title":"Nord. J. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kinon, B.J. (2019). The group of treatment resistant schizophrenias. Heterogeneity in treatment resistant schizophrenia (TRS). Front. Psychiatry, 9.","DOI":"10.3389\/fpsyt.2018.00757"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1038\/s41386-019-0577-3","article-title":"Heterogeneity and efficacy of antipsychotic treatment for schizophrenia with or without treatment resistance: A meta-analysis","volume":"45","author":"Mizuno","year":"2020","journal-title":"Neuropsychopharmacology"},{"key":"ref_11","first-page":"221","article-title":"Psychotic disorders as a framework for precision psychiatry","volume":"19","author":"Coutts","year":"2023","journal-title":"Nat. Rev. Neurol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ponce-Correa, F., Caqueo-Ur\u00edzar, A., Berrios, R., and Escobar-Soler, C. (2023). Defining recovery in schizophrenia: A review of outcome studies. Psychiatry Res., 322.","DOI":"10.1016\/j.psychres.2023.115134"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Desalegn, D., Girma, S., and Abdeta, T. (2020). Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229514"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"199","DOI":"10.2147\/PROM.S454845","article-title":"Systematic Literature Review of Studies Reporting Measures of Functional Outcome or Quality of Life in People with Negative Symptoms of Schizophrenia","volume":"15","author":"Pollock","year":"2024","journal-title":"Patient Relat. Outcome Meas."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.jtho.2015.12.108","article-title":"A novel clinical prediction model for prognosis in malignant pleural mesothelioma using decision tree analysis","volume":"11","author":"Brims","year":"2016","journal-title":"J. Thorac. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s41746-022-00589-7","article-title":"Natural language processing applied to mental illness detection: A narrative review","volume":"5","author":"Zhang","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s41746-022-00631-8","article-title":"The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review","volume":"5","author":"Alhuwail","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e816","DOI":"10.1016\/S2589-7500(22)00152-2","article-title":"The promise of a model-based psychiatry: Building computational models of mental ill health","volume":"4","author":"Hauser","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1016\/j.artint.2007.10.009","article-title":"From here to human-level AI","volume":"171","author":"McCarthy","year":"2007","journal-title":"Artif. Intell."},{"key":"ref_20","unstructured":"Samoili, S., Cobo, M.L., G\u00f3mez, E., De Prato, G., Mart\u00ednez-Plumed, F., and Delipetrev, B. (2020). AI Watch. Defining Artificial Intelligence. Towards an Operational Definition and Taxonomy of Artificial Intelligence, Joint Research Centre."},{"key":"ref_21","unstructured":"McCarthy, J. (2007). What is Artificial Intelligence, Stanford University. Available online: https:\/\/www-formal.stanford.edu\/jmc\/whatisai\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Morales, E.F., and Escalante, H.J. (2022). A brief introduction to supervised, unsupervised, and reinforcement learning. Biosignal Processing and Classification Using Computational Learning and Intelligence, Elsevier.","DOI":"10.1016\/B978-0-12-820125-1.00017-8"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tate, A.E., McCabe, R.C., Larsson, H., Lundstr\u00f6m, S., Lichtenstein, P., and Kuja-Halkola, R. (2020). Predicting mental health problems in adolescence using machine learning techniques. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230389"},{"key":"ref_25","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":"ref_26","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1093\/schbul\/sbz076","article-title":"Anterior hippocampal\u2013cortical functional connectivity distinguishes antipsychotic na\u00efve first-episode psychosis patients from controls and may predict response to second-generation antipsychotic treatment","volume":"46","author":"Blessing","year":"2020","journal-title":"Schizophr. Bull."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1038\/s41380-018-0106-5","article-title":"Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity","volume":"25","author":"Cao","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1002\/aur.2151","article-title":"Predicting aggression to others in youth with autism using a wearable biosensor","volume":"12","author":"Goodwin","year":"2019","journal-title":"Autism Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1038\/s41398-020-00962-8","article-title":"A machine-learning framework for robust and reliable prediction of short-and long-term treatment response in initially antipsychotic-na\u00efve schizophrenia patients based on multimodal neuropsychiatric data","volume":"10","author":"Ambrosen","year":"2020","journal-title":"Transl. Psychiatry"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cui, L.B., Zhang, Y.J., Lu, H.L., Liu, L., Zhang, H.J., Fu, Y.F., Wu, X.S., Xu, Y.Q., Li, X.S., and Qiao, Y.T. (2021). Thalamus radiomics-based disease identification and prediction of early treatment response for schizophrenia. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.682777"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/S2215-0366(16)30171-7","article-title":"Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: A machine learning approach","volume":"3","author":"Koutsouleris","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e261","DOI":"10.1016\/S2589-7500(19)30121-9","article-title":"Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: A machine learning approach","volume":"1","author":"Leighton","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_33","first-page":"1","article-title":"Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection","volume":"11","author":"Lin","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1017\/S0033291718003070","article-title":"Predicting first-episode psychosis patients who will never relapse over 10 years","volume":"49","author":"Hui","year":"2019","journal-title":"Psychol. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, L., Rami, F.Z., Lee, B.M., Kim, W.-S., Kang, C.Y., Kim, S.-W., Lee, B.J., Kim, J.J., Yu, J.-C., and Lee, K.Y. (2023). Predictors of full recovery in patients with early stage schizophrenia spectrum disorders. Psychiatry Res., 320.","DOI":"10.1016\/j.psychres.2022.115035"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bozzatello, P., Bellino, S., and Rocca, P. (2019). Predictive factors of treatment resistance in first episode of psychosis: A systematic review. Front. Psychiatry, 10.","DOI":"10.3389\/fpsyt.2019.00067"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"205","DOI":"10.31887\/DCNS.2014.16.4\/mcarbon","article-title":"Clinical predictors of therapeutic response to antipsychotics in schizophrenia","volume":"16","author":"Carbon","year":"2014","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Arrasate, M., Gonz\u00e1lez-Ortega, I., Garc\u00eda-Aloc\u00e9n, A., Alberich, S., Zorrilla, I., and Gonz\u00e1lez-Pinto, A. (2016). Prognostic value of affective symptoms in first-admission psychotic patients. Int. J. Mol. Sci., 17.","DOI":"10.3390\/ijms17071039"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Magrangeas, T.T., Kolliakou, A., Sanyal, J., Patel, R., and Stewart, R. (2022). Investigating the relationship between thought interference, somatic passivity and outcomes in patients with psychosis: A natural language processing approach using a clinical records search platform in south London. BMJ Open, 12.","DOI":"10.1136\/bmjopen-2021-057433"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_41","unstructured":"Joanna Briggs Institute (2024, February 01). Critical Appraisal Tools. Available online: https:\/\/jbi.global\/critical-appraisal-tools."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3342","DOI":"10.1002\/hbm.25020","article-title":"Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors","volume":"41","author":"Kottaram","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1093\/schbul\/sbab107","article-title":"Prediction of early symptom remission in two independent samples of first-episode psychosis patients using machine learning","volume":"48","author":"Soldatos","year":"2022","journal-title":"Schizophr. Bull."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2754","DOI":"10.1017\/S0033291718003781","article-title":"Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-na\u00efve schizophrenia patients","volume":"49","author":"Ebdrup","year":"2019","journal-title":"Psychol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.pnpbp.2018.12.005","article-title":"Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort","volume":"92","author":"Fond","year":"2019","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.schres.2023.10.030","article-title":"The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach","volume":"262","author":"Cysouw","year":"2023","journal-title":"Schizophr. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/0747-5632(94)00022-A","article-title":"Sensitivity profile of 41 psychiatric parameters determined by neural network in relation to 8-week outcome","volume":"11","author":"Modai","year":"1995","journal-title":"Comput. Hum. Behav."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1038\/s41386-019-0322-y","article-title":"Structural similarity networks predict clinical outcome in early-phase psychosis","volume":"44","author":"Homan","year":"2019","journal-title":"Neuropsychopharmacology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1176\/appi.ajp.2015.14121571","article-title":"Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment","volume":"173","author":"Sarpal","year":"2016","journal-title":"Am. J. Psychiatry"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1002\/hbm.25286","article-title":"Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging","volume":"42","author":"Smucny","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/JBHI.2023.3265684","article-title":"Psychotic Relapse Prediction in Schizophrenia Patients Using a Personalized Mobile Sensing-Based Supervised Deep Learning Model","volume":"27","author":"Lamichhane","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Leighton, S.P., Krishnadas, R., Chung, K., Blair, A., Brown, S., Clark, S., Sowerbutts, K., Schwannauer, M., Cavanagh, J., and Gumley, A.I. (2019). Predicting one-year outcome in first episode psychosis using machine learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212846"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1017\/S0033291711002005","article-title":"Individualized prediction of illness course at the first psychotic episode: A support vector machine MRI study","volume":"42","author":"Reinders","year":"2012","journal-title":"Psychol. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1111\/ejn.15046","article-title":"Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy","volume":"53","author":"Cui","year":"2021","journal-title":"Eur. J. Neurosci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"847","DOI":"10.2147\/NDT.S280757","article-title":"A random forest model for predicting social functional improvement in Chinese patients with schizophrenia after 3 months of atypical antipsychotic monopharmacy: A cohort study","volume":"17","author":"Li","year":"2021","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liu, W., Fang, P., Guo, F., Qiao, Y., Zhu, Y., and Wang, H. (2022). Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia. Dis. Markers, 2022.","DOI":"10.1155\/2022\/1853002"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, M., Hu, K., Fan, L., Yan, H., Li, P., Jiang, T., and Liu, B. (2022). Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.848205"},{"key":"ref_58","first-page":"1","article-title":"Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions","volume":"11","author":"Lin","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wu, C.-S., Luedtke, A.R., Sadikova, E., Tsai, H.-J., Liao, S.-C., Liu, C.-C., Gau, S.S.-F., VanderWeele, T.J., and Kessler, R.C. (2020). Development and validation of a machine learning individualized treatment rule in first-episode schizophrenia. JAMA Netw. Open, 3.","DOI":"10.1001\/jamanetworkopen.2019.21660"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"34","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":"Burger","year":"2021","journal-title":"Npj Schizophr."},{"key":"ref_61","unstructured":"Schie, B.v. (2022). Assessing the Role of Cytokines in Psychosis Prognosis Prediction: A Machine Learning Approach. [Master\u2019s Thesis, Utrecht University]."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1111\/cts.13035","article-title":"Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials","volume":"14","author":"Podichetty","year":"2021","journal-title":"Clin. Transl. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.schres.2019.05.044","article-title":"Identifying schizophrenia subgroups using clustering and supervised learning","volume":"214","author":"Talpalaru","year":"2019","journal-title":"Schizophr. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s13312-011-0055-4","article-title":"Receiver operating characteristic (ROC) curve for medical researchers","volume":"48","author":"Kumar","year":"2011","journal-title":"Indian Pediatr."},{"key":"ref_65","unstructured":"Allwright, S. (2023, February 17). What Is a Good Balanced Accuracy Score? Simply Explainedat Is a Good Balanced Accuracy Score? Simply Explained. Available online: https:\/\/stephenallwright.com\/balanced-accuracy\/#:~:text=Much%20like%20accuracy%2C%20balanced%20accuracy,Between%200.7%20and%200.9%20%2D%20Good."},{"key":"ref_66","unstructured":"Glen, S. (2023, February 17). RMSE: Root Mean Square Error. Statistics How To 2023. Available online: https:\/\/www.statisticshowto.com\/probability-and-statistics\/regression-analysis\/rmse-root-mean-square-error\/."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1176\/appi.ajp.2008.07101562","article-title":"The anatomy of first-episode and chronic schizophrenia: An anatomical likelihood estimation meta-analysis","volume":"165","author":"Glahn","year":"2008","journal-title":"Am. J. Psychiatry"},{"key":"ref_68","first-page":"1","article-title":"Predictors of outcome in early-onset psychosis: A systematic review","volume":"1","author":"Fraguas","year":"2015","journal-title":"Npj Schizophr."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"18451","DOI":"10.4088\/JCP.15r10003","article-title":"Risk prediction models in psychiatry: Toward a new frontier for the prevention of mental illnesses","volume":"78","author":"Bernardini","year":"2017","journal-title":"J. Clin. Psychiatry"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1017\/S0033291721003871","article-title":"Machine learning in the prediction of depression treatment outcomes: A systematic review and meta-analysis","volume":"51","author":"Sajjadian","year":"2021","journal-title":"Psychol. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/s41398-021-01286-x","article-title":"Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: A systematic review and meta-analysis","volume":"11","author":"Cohen","year":"2021","journal-title":"Transl. Psychiatry"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.jad.2021.08.127","article-title":"Exploring machine learning to predict depressive relapses of bipolar disorder patients","volume":"295","author":"Lafer","year":"2021","journal-title":"J. Affect. Disord."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Shao, Y., Cheng, Y., Gottipati, S., and Zeng-Treitler, Q. (2023). Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data. Appl. Sci., 13.","DOI":"10.3390\/app13031552"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Soria, C., Arroyo, Y., Torres, A.M., Redondo, M., Basar, C., and Mateo, J. (2023). Method for Classifying Schizophrenia Patients Based on Machine Learning. J. Clin. Med., 12.","DOI":"10.3390\/jcm12134375"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s11920-022-01399-0","article-title":"Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment","volume":"24","author":"Ferrara","year":"2022","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1038\/s41398-023-02371-z","article-title":"Machine learning methods to predict outcomes of pharmacological treatment in psychosis","volume":"13","author":"Bondi","year":"2023","journal-title":"Transl. Psychiatry"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1034\/j.1600-0447.2002.01351.x","article-title":"Influence of gender on social outcome in schizophrenia","volume":"106","author":"Usall","year":"2002","journal-title":"Acta Psychiatr. Scand."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-244X-12-150","article-title":"A systematic review of the long-term outcome of early onset schizophrenia","volume":"12","author":"Clemmensen","year":"2012","journal-title":"BMC Psychiatry"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"417","DOI":"10.3109\/09540261.2010.515205","article-title":"Sex differences in schizophrenia","volume":"22","author":"Abel","year":"2010","journal-title":"Int. Rev. Psychiatry"},{"key":"ref_80","first-page":"37","article-title":"Why sex differences in schizophrenia?","volume":"1","author":"Li","year":"2016","journal-title":"J. Transl. Neurosci."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Hatzimanolis, A., Stefanatou, P., Kattoulas, E., Ralli, I., Dimitrakopoulos, S., Foteli, S., Kosteletos, I., Mantonakis, L., Selakovic, M., and Soldatos, R.-F. (2020). Familial and socioeconomic contributions to premorbid functioning in psychosis: Impact on age at onset and treatment response. Eur. Psychiatry, 63.","DOI":"10.1192\/j.eurpsy.2020.41"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1001\/archpsyc.1974.01760130021003","article-title":"The prediction of outcome in schizophrenia: II. Relationships between predictor and outcome variables: A report from the WHO International Pilot Study of Schizophrenia","volume":"31","author":"Strauss","year":"1974","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12888-021-03488-5","article-title":"Associations of living arrangements with symptoms and functioning in schizophrenia","volume":"21","author":"Ang","year":"2021","journal-title":"BMC Psychiatry"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1017\/S0033291721002403","article-title":"Effects of socioeconomic status in cognition of people with schizophrenia: Results from a Latin American collaboration network with 1175 subjects","volume":"52","author":"Czepielewski","year":"2022","journal-title":"Psychol. Med."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Sweeney, S., Air, T., Zannettino, L., and Galletly, C. (2015). Psychosis, socioeconomic disadvantage, and health service use in South Australia: Findings from the second Australian national survey of psychosis. Front. Public Health, 3.","DOI":"10.3389\/fpubh.2015.00259"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12888-020-2484-x","article-title":"Predictors of outcome in early onset schizophrenia: A 10-year follow-up study","volume":"20","author":"Xu","year":"2020","journal-title":"BMC Psychiatry"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.schres.2020.01.002","article-title":"A symptom combination predicting treatment-resistant schizophrenia\u2013A strategy for real-world clinical practice","volume":"218","author":"Ortiz","year":"2020","journal-title":"Schizophr. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.scog.2015.08.001","article-title":"Poor premorbid school performance, but not severity of illness, predicts cognitive decline in schizophrenia in midlife","volume":"2","author":"Rannikko","year":"2015","journal-title":"Schizophr. Res. Cogn."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s11414-008-9151-1","article-title":"Social support, activities, and recovery from serious mental illness: STARS study findings","volume":"36","author":"Hendryx","year":"2009","journal-title":"J. Behav. Health Serv. Res."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.schres.2010.10.020","article-title":"Prediction of a single psychotic episode: A 7.5-year, prospective study in first-episode psychosis","volume":"125","author":"Gleeson","year":"2011","journal-title":"Schizophr. Res."},{"key":"ref_91","first-page":"172","article-title":"The relationship of marital status and clinical characteristics in middle-aged and older patients with schizophrenia and depressive symptoms","volume":"22","author":"Nyer","year":"2010","journal-title":"Ann Clin Psychiatry"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.schres.2016.10.034","article-title":"Marriage and outcomes of people with schizophrenia in rural China: 14-year follow-up study","volume":"182","author":"Ran","year":"2017","journal-title":"Schizophr. Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/1475-6773.13606","article-title":"Socioeconomic status and the effectiveness of treatment for first-episode psychosis","volume":"56","author":"Bennett","year":"2021","journal-title":"Health Serv. Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"803","DOI":"10.31128\/AJGP-04-20-5384","article-title":"Physical exercise and the patient with schizophrenia","volume":"49","author":"Viljoen","year":"2020","journal-title":"Aust. J. Gen. Pract."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s13033-021-00464-0","article-title":"Lifetime relapse and its associated factors among people with schizophrenia spectrum disorders who are on follow up at Comprehensive Specialized Hospitals in Amhara region, Ethiopia: A cross-sectional study","volume":"15","author":"Moges","year":"2021","journal-title":"Int. J. Ment. Health Syst."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Lin, D., Joshi, K., Keenan, A., Shepherd, J., Bailey, H., Berry, M., Wright, J., Meakin, S., Benson, C., and Kim, E. (2021). Associations between relapses and psychosocial outcomes in patients with schizophrenia in real-world settings in the United States. Front. Psychiatry, 12.","DOI":"10.3389\/fpsyt.2021.695672"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.schres.2013.05.016","article-title":"The evidence for illness progression after relapse in schizophrenia","volume":"148","author":"Emsley","year":"2013","journal-title":"Schizophr. Res."},{"key":"ref_98","doi-asserted-by":"crossref","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","year":"2023","journal-title":"Psychol. Med."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1177\/0706743719870515","article-title":"The core deficit of classical schizophrenia: Implications for predicting the functional outcome of psychotic illness and developing effective treatments","volume":"64","author":"Liddle","year":"2019","journal-title":"Can. J. Psychiatry"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/08039480801959307","article-title":"Negative symptoms in schizophrenia\u2014A review","volume":"62","author":"Miettunen","year":"2008","journal-title":"Nord. J. Psychiatry"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"525","DOI":"10.12740\/PP\/OnlineFirst\/100697","article-title":"Recommendations for the treatment of schizophrenia with negative symptoms. Standards of pharmacotherapy by the Polish Psychiatric Association (Polskie Towarzystwo Psychiatryczne), part 2","volume":"53","author":"Szulc","year":"2019","journal-title":"Psychiatr. Pol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/acps.12030","article-title":"Psychopathological long-term outcome of schizophrenia\u2014A review","volume":"127","author":"Lang","year":"2013","journal-title":"Acta Psychiatr. Scand."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1037\/abn0000188","article-title":"Validating dimensions of psychosis symptomatology: Neural correlates and 20-year outcomes","volume":"125","author":"Kotov","year":"2016","journal-title":"J. Abnorm. Psychol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.schres.2019.12.011","article-title":"Depressive symptoms during first episode psychosis and functional outcome: A systematic review and meta-analysis","volume":"218","author":"McGinty","year":"2020","journal-title":"Schizophr. Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"320","DOI":"10.4103\/0019-5545.74306","article-title":"Predictors of long-term outcome of first-episode schizophrenia: A ten-year follow-up study","volume":"52","author":"Shrivastava","year":"2010","journal-title":"Indian J. Psychiatry"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"11814","DOI":"10.4088\/JCP.17m12007","article-title":"Metabolic syndrome and illness severity predict relapse at 1-year follow-up in schizophrenia: The FACE-SZ cohort","volume":"79","author":"Godin","year":"2018","journal-title":"J. Clin. Psychiatry"},{"key":"ref_107","unstructured":"Foiselle, M., Barbosa, S., Godin, O., Wu, C.-L., Boukouaci, W., Andre, M., Aouizerate, B., Berna, F., Barau, C., and Capdevielle, D. (2022). Immuno-metabolic profile of patients with psychotic disorders and metabolic syndrome. Results from the FACE-SZ cohort. Brain Behav. Immun-Health, 22."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1177\/0269881108100777","article-title":"Cognitive effects of antipsychotic dosage and polypharmacy: A study with the BACS in patients with schizophrenia and schizoaffective disorder","volume":"24","author":"Elie","year":"2010","journal-title":"J. Psychopharmacol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/S0140-6736(03)13306-5","article-title":"New generation antipsychotics versus low-potency conventional antipsychotics: A systematic review and meta-analysis","volume":"361","author":"Leucht","year":"2003","journal-title":"Lancet"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0140-6736(08)61764-X","article-title":"Second-generation versus first-generation antipsychotic drugs for schizophrenia: A meta-analysis","volume":"373","author":"Leucht","year":"2009","journal-title":"Lancet"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1192\/bjp.146.3.229","article-title":"Schizophrenia with good and poor outcome. I: Early clinical features, response to neuroleptics and signs of organic dysfunction","volume":"146","author":"Kolakowska","year":"1985","journal-title":"Br. J. Psychiatry"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.schres.2006.05.004","article-title":"Antipsychotic medication and cognitive function in schizophrenia","volume":"86","author":"Hori","year":"2006","journal-title":"Schizophr. Res."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1192\/bjp.bp.115.177261","article-title":"Clozapine v. first-and second-generation antipsychotics in treatment-refractory schizophrenia: Systematic review and meta-analysis","volume":"209","author":"Siskind","year":"2016","journal-title":"Br. J. Psychiatry"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/08039488.2020.1811374","article-title":"Effectiveness of clozapine on quality of life and functioning in patients with treatment-resistant schizophrenia","volume":"75","author":"Verma","year":"2021","journal-title":"Nord. J. Psychiatry"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.euroneuro.2021.04.023","article-title":"Comparative outcomes in electroconvulsive therapy (ECT): A naturalistic comparison between outcomes in psychosis, mania, depression, psychotic depression and catatonia","volume":"51","author":"Tor","year":"2021","journal-title":"Eur. Neuropsychopharmacol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/S0920-9964(97)00126-6","article-title":"MRI correlates of treatment response in first episode psychosis","volume":"30","author":"Zipursky","year":"1998","journal-title":"Schizophr. Res."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.schres.2006.06.016","article-title":"White matter fractional anisotropy and outcome in schizophrenia","volume":"87","author":"Mitelman","year":"2006","journal-title":"Schizophr. Res."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1016\/j.pnpbp.2009.07.025","article-title":"Gray matter morphology and the level of functioning in one-year follow-up of first-episode schizophrenia patients","volume":"33","author":"Kasparek","year":"2009","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.schres.2009.07.014","article-title":"Progressive gray matter changes in first episode schizophrenia: A 4-year longitudinal magnetic resonance study using VBM","volume":"114","author":"Falcon","year":"2009","journal-title":"Schizophr. Res."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0006-3223(98)00175-9","article-title":"Cerebellar morphology as a predictor of symptom and psychosocial outcome in schizophrenia","volume":"45","author":"Wassink","year":"1999","journal-title":"Biol. Psychiatry"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.eurpsy.2013.10.006","article-title":"Associations between brain morphology and outcome in schizophrenia in a general population sample","volume":"29","author":"Juola","year":"2014","journal-title":"Eur. Psychiatry"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1017\/S0033291713001712","article-title":"Bilateral hippocampal increase following first-episode psychosis is associated with good clinical, functional and cognitive outcomes","volume":"44","author":"Lappin","year":"2014","journal-title":"Psychol. Med."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.schres.2021.09.004","article-title":"Resting-state functional connectivity predictors of treatment response in schizophrenia\u2013A systematic review and meta-analysis","volume":"237","author":"Mehta","year":"2021","journal-title":"Schizophr. Res."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1038\/s41380-018-0269-0","article-title":"Baseline brain structural and functional predictors of clinical outcome in the early course of schizophrenia","volume":"25","author":"Doucet","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1001\/jamapsychiatry.2014.1734","article-title":"Antipsychotic treatment and functional connectivity of the striatum in first-episode schizophrenia","volume":"72","author":"Sarpal","year":"2015","journal-title":"JAMA Psychiatry"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.pscychresns.2016.10.008","article-title":"Altered resting-state regional homogeneity after 13 weeks of paliperidone injection treatment in schizophrenia patients","volume":"258","author":"Bai","year":"2016","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ebiom.2019.07.022","article-title":"Enhanced baseline activity in the left ventromedial putamen predicts individual treatment response in drug-naive, first-episode schizophrenia: Results from two independent study samples","volume":"46","author":"Li","year":"2019","journal-title":"EBioMedicine"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1038\/npp.2009.192","article-title":"Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia","volume":"35","author":"Sambataro","year":"2010","journal-title":"Neuropsychopharmacology"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.ebiom.2018.09.012","article-title":"Disrupted asymmetry of inter-and intra-hemispheric functional connectivity in patients with drug-naive, first-episode schizophrenia and their unaffected siblings","volume":"36","author":"Zhu","year":"2018","journal-title":"EBioMedicine"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1093\/schbul\/sbv228","article-title":"Aberrant hippocampal connectivity in unmedicated patients with schizophrenia and effects of antipsychotic medication: A longitudinal resting state functional MRI study","volume":"42","author":"Kraguljac","year":"2016","journal-title":"Schizophr. 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