[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117213-en":3,"doc-seo-117213-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117213,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Editorial: Machine learning and psychosis - Diagnosis, prognosis and treatment","Editorial article introducing a research topic on using machine learning to address unmet clinical needs in psychosis. It highlights the severity of psychosis, low recovery and relapse rates, and major burdens including economic costs, premature mortality, and suicide risk. The editorial frames difficulties in early diagnosis, biomarker identification, and treatment response, and argues that computational, data-driven approaches can support differential diagnosis, outcome prediction, and understanding biological heterogeneity through multimodal data.","TYPE Editorial  \nPUBLISHED 21 February 2023 DOI 10. 3389/fpsyt.2023.1133072  \nOPEN ACCESS  \nEDITED AND REVIEWED BY  \nFrancisco Rodrigues,  \nUniversity of São Paulo, Brazil  \n*CORRESPONDENCE  \nAlessandro Pigoni  \n [ale.pigoni@gmail.com](ale.pigoni@gmail.com)  \nSPECIALTY SECTION  \nThis article was submitted to Computational Psychiatry, a section of the journal Frontiers in Psychiatry  \nRECEIVED 28 December 2022  \nACCEPTED 06 February 2023  \nPUBLISHED 21 February 2023  \nCITATION  \nD’Ambrosio E, Abrol A and Pigoni A (2023) Editorial: Machine learning and psychosis:  \nDiagnosis, prognosis and treatment.  \nFront. Psychiatry 14:1133072 .  \ndoi: 10.3389/fpsyt.2023.1133072  \nCOPYRIGHT  \n© 2023 D’Ambrosio, Abrol and Pigoni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEditorial: Machine learning and  \npsychosis: Diagnosis, prognosis and treatment  \nEnrico D’Ambrosio1,2 , Anees Abrol3 and Alessandro Pigoni4,5*  \n1 Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy, 2 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 3Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States, 4 Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy, 5 Social and A􀀀ective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy  \nKEYWORDS  \nmachine learning (ML), psychosis, schizophrenia, electronic health records, genetics  \nEditorial on the Research Topic  \nMachine learning and psychosis: Diagnosis, prognosis and treatment  \nPsychosis is a severe condition associated with considerable disability that a􀀓ects patients and their families, causing substantial economic burdens in terms of lost productivity and direct costs (medications, hospitalizations, etc.) (1, 2) . Individuals a􀀓ected by psychosis still su􀀓er from a low recovery rate and have a high likelihood of relapse. Moreover, patients a􀀓ected by psychosis su􀀓er from premature mortality due to cardio-metabolic complications and a higher risk for suicide (3–5) .  \nFor these reasons, psychosis prevention and early treatment are public health priorities. However, to date, psychosis still presents many unresolved challenges in diagnosis, identi􀀂cation of speci􀀂c biomarkers, and treatments, especially in its early stages. Diagnoses are often blurred at presentation, and subgroups of individuals do not achieve a symptomatic and functional recovery, despite receiving specialized care. An increasing amount of evidence supports implementing early detection and intervention in psychotic disorders (6), with bene􀀂ts in terms of a lower rate of relapse and overall better quality of life and functioning (7) .  \nA precise de􀀂nition of outcomes and response to treatments assessed readily on presentation would certainly impact patient management. However, so far, studies have struggled to identify valid biomarkers of psychosis trajectories and treatment response (8) . Within the many strategies to improve the detection and outcomes of psychosis, machine learning (ML) techniques have seen tremendous development as they are resourceful in handling complex problems. ML are data-driven methods that deal with multivariate patterns by going beyond group-level analyses (9) . These techniques have the potential to learn subtle data attributes, analyze mass","cbCairY5b8vblLXy","https://ap.wps.com/l/cbCairY5b8vblLXy","pdf",100944,1,3,"English","en",105,"# Editorial on the Research Topic\n## Psychosis burden and unresolved challenges\n## Role of machine learning in diagnosis, prognosis, and treatment\n## Electronic health records and prediction of relapse\n## Natural language processing and model generalization","[{\"question\":\"Why are early detection and intervention important in psychosis?\",\"answer\":\"Psychosis still has unresolved challenges in early-stage diagnosis and treatment outcomes. The editorial notes that early detection and intervention can reduce relapse rates and improve overall quality of life and functioning.\"},{\"question\":\"What problems do clinicians face when defining outcomes and treatment response?\",\"answer\":\"Studies have struggled to identify valid biomarkers for psychosis trajectories and treatment response. The editorial emphasizes the need for outcomes and response measures assessed readily at presentation to improve patient management.\"},{\"question\":\"How can machine learning support clinical decision-making for psychosis?\",\"answer\":\"Machine learning techniques can learn multivariate patterns, integrate multimodal data such as neuroimaging, clinical data, and genetics, and help uncover risk patterns and biomarkers. This supports differential diagnosis, prognosis, and treatment outcome prediction.\"}]","Editorial: Machine learning and psychosis - Diagnosis, prognosis and treatment | PDF",1785674438,8,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"editorial-machine-learning-and-psychosis-diagnosis-prognosis-and-treatment","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":21},"https://docshare.wps.com/document/healthcare/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/editorial-machine-learning-and-psychosis-diagnosis-prognosis-and-treatment/117213/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why are early detection and intervention important in psychosis?","Question",{"text":74,"@type":75},"Psychosis still has unresolved challenges in early-stage diagnosis and treatment outcomes. The editorial notes that early detection and intervention can reduce relapse rates and improve overall quality of life and functioning.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What problems do clinicians face when defining outcomes and treatment response?",{"text":79,"@type":75},"Studies have struggled to identify valid biomarkers for psychosis trajectories and treatment response. The editorial emphasizes the need for outcomes and response measures assessed readily at presentation to improve patient management.",{"name":81,"@type":72,"acceptedAnswer":82},"How can machine learning support clinical decision-making for psychosis?",{"text":83,"@type":75},"Machine learning techniques can learn multivariate patterns, integrate multimodal data such as neuroimaging, clinical data, and genetics, and help uncover risk patterns and biomarkers. 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