[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119346-en":3,"doc-seo-119346-105":29,"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119346,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Detection of formal thought disorders in child and adolescent psychosis using machine learning and neuropsychometric data","The study evaluates whether neuropsychological testing combined with machine learning can distinguish formal thought disorder (FTD) in early-onset psychosis from cases without FTD. A cohort of 27 young participants underwent Iowa Gambling Task and Simple Reaction Time assessments, alongside medication load expressed in chlorpromazine equivalents. Multiple classifiers, including logistic regression, support vector machines, random forest, and XGBoost, were trained and cross-validated. Logistic regression achieved moderate-to-good discrimination (ROC AUC 0.850), supported by task performance and reaction-time variability, enabling timely identification.","TYPE Brief Research Report PUBLISHED 17 March 2025  \nDOI 10.3389/fpsyt.2025.1550571  \nOPEN ACCESS  \nEDITED BY  \nAmit Singhal,  \nNetaji Subhas University of Technology, India  \nREVIEWED BY  \nAnimesh Kumar Paul, University of Alberta, Canada Alexandre Hudon,  \nMontreal University, Canada  \n*CORRESPONDENCE  \nPrzemysław T. Zakowicz  \n [pzakowicz@neurologicalsociety.org](pzakowicz@neurologicalsociety.org)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 23 December 2024  \nACCEPTED 03 March 2025  \nPUBLISHED 17 March 2025  \nCITATION  \nZakowicz PT, Brzezicki MA, Levidiotis C, Kim S, Wejkuc´ O, Wisniewska Z, Biernaczyk D and Remberk B (2025) Detection of formal thought disorders in child and adolescent psychosis using machine learning and neuropsychometric data.  \nFront. Psychiatry 16:1550571 .  \ndoi: 10.3389/fpsyt.2025.1550571  \nCOPYRIGHT  \n© 2025 Zakowicz, Brzezicki, Levidiotis, Kim, Wejkuc´, Wisniewska, Biernaczyk and Remberk. 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.  \nDetection of formal thought disorders in child and adolescent psychosis using machine learning and neuropsychometric data  \nPrzemysław T. Zakowicz 1*†, Maksymilian A. Brzezicki 2†, Charalampos Levidiotis 3, Sojeong Kim 3, Oskar Wejku´c1, Zuzanna Wisniewska 1, Dominika Biernaczyk 1 and Barbara Remberk 4  \n1Collegium Medicum, University of Zielona Gora, Zielona Góra, Poland, 2 Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 3 Department of Medicine, Poznan University of Medical Sciences, Poznan, Poland, 4 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Neurology, Warsaw, Poland  \nIntroduction: Formal Thought Disorder (FTD) is a significant clinical feature of earlyonset psychosis, often associated with poorer outcomes. Current diagnostic methods rely on clinical assessment, which can be subjective and timeconsuming. This study aimed to investigate the potential of neuropsychological tests and machine learning to differentiate individuals with and without FTD.  \nMethods: A cohort of 27 young people with early-onset psychosis was included. Participants underwent neuropsychological assessment using the Iowa Gambling Task (IGT) and Simple Reaction Time (SRT) tasks. A range of machine learning models (Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)) were employed to classify participants into FTD-positive and FTD-negative groups based on these neuropsychological measures and their antipsychotic regimen (medication load in chlorpromazine equivalents) .  \nResults: The best performing machine learning model was LR with mean +/ -standard deviation of cross validation Receiver Operating Characteristic Area Under Curve (ROC AUC) score of 0 . 850 (+/ -0. 133), indicating moderate-togood discriminatory performance. Key features contributing to the model ’s accuracy included IGT card selections, SRT reaction time (most notably standard deviation) and chlorpromazine equivalent milligrams. The model correctly classified 24 out of 27 participants.  \nDiscussion: This study demonstrates the feasibility of using neuropsychological tests and machine learning to identify FTD in early-onset psychosis. Early identification of FTD may facilitate targeted interventions and improve clinical outcomes. Further research is needed to validate these findings in larger, more diverse populations and to explore the underlying neurocognitive mechanisms.  \nKEYWORDS  \nchild and adolescent psychosis, machine learning, cognitive psyc","cbCailXW5FrVPWEr","https://ap.wps.com/l/cbCailXW5FrVPWEr","pdf",742550,1,"English","en",105,"# Introduction\n## Formal thought disorders and early-onset psychosis\n## Limitations of clinical assessment\n# Methods\n## Participants and study design\n## Neuropsychological tasks and features\n## Machine learning classifiers\n# Results\n## Model performance and key predictive features\n# Discussion\n## Clinical feasibility and implications\n## Future validation needs","[{\"question\":\"What is the study trying to detect in young patients with psychosis?\",\"answer\":\"The study aims to detect formal thought disorders (FTD) in child and adolescent/early-onset psychosis by distinguishing FTD-positive from FTD-negative individuals.\"},{\"question\":\"Which neuropsychological measures and data were used for the machine learning models?\",\"answer\":\"Participants completed the Iowa Gambling Task (IGT) and Simple Reaction Time (SRT) tasks, and the models also used antipsychotic regimen information expressed as medication load in chlorpromazine equivalents.\"},{\"question\":\"How well did the best model perform, and what contributed most to its accuracy?\",\"answer\":\"Logistic regression performed best with a cross-validation ROC AUC of 0.850 (moderate-to-good discrimination). Key contributors included IGT card selections, SRT reaction-time variability (notably standard deviation), and chlorpromazine-equivalent milligrams.\"}]","Detection of formal thought disorders in child and adolescent psychosis using machine learning and neuropsychometric data | PDF",1785723809,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"detection-of-formal-thought-disorders-in-child-and-adolescent-psychosis-using-machine-learning-and-neuropsychometric-data","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/detection-of-formal-thought-disorders-in-child-and-adolescent-psychosis-using-machine-learning-and-neuropsychometric-data/119346/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",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},"What is the study trying to detect in young patients with psychosis?","Question",{"text":74,"@type":75},"The study aims to detect formal thought disorders (FTD) in child and adolescent/early-onset psychosis by distinguishing FTD-positive from FTD-negative individuals.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which neuropsychological measures and data were used for the machine learning models?",{"text":79,"@type":75},"Participants completed the Iowa Gambling Task (IGT) and Simple Reaction Time (SRT) tasks, and the models also used antipsychotic regimen information expressed as medication load in chlorpromazine equivalents.",{"name":81,"@type":72,"acceptedAnswer":82},"How well did the best model perform, and what contributed most to its accuracy?",{"text":83,"@type":75},"Logistic regression performed best with a cross-validation ROC AUC of 0.850 (moderate-to-good discrimination). Key contributors included IGT card selections, SRT reaction-time variability (notably standard deviation), and chlorpromazine-equivalent milligrams.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]