[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117383-en":3,"doc-seo-117383-105":30,"detail-sidebar-cat-0-en-105":91},{"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},117383,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Predicting conversion to psychosis using machine learning: response to Cannon","Machine learning is evaluated for predicting conversion to psychosis in individuals at clinical high risk (CHR) for psychosis. The study addresses the need to validate a predictive model trained on the NAPLS-3 dataset by testing generalizability on an independent dataset, NAPLS-2, using the same machine-learning algorithms. Results show significant feature differences between NAPLS-2 and NAPLS-3, with models performing above chance yet failing to reach previously reported performance levels. Naive Bayes and random forest yielded the best overall results.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nPredicting conversion to psychosis using machine learning: response to Cannon.  \nPermalink  \n[https://escholarship.org/uc/item/2n0141q4](https://escholarship.org/uc/item/2n0141q4)  \nAuthors  \nSmucny, Jason  \nCannon, Tyrone Bearden, Carrie et al.  \nPublication Date  \n2024  \nDOI  \n10.3389/fpsyt.2024.1520173  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nTYPE Brief Research Report PUBLISHED 15 January 2025 DOI 10.3389/fpsyt.2024.1520173  \nOPEN ACCESS  \nEDITED BY  \nJennifer Larimore,  \nAgnes Scott College, United States  \nREVIEWED BY  \nNikolaos Smyrnis,  \nNational and Kapodistrian University of Athens, Greece  \nAyse Ulgen,  \nNottingham Trent University, United Kingdom  \n*CORRESPONDENCE  \nJason Smucny  \n[jsmucny@ucdavis.edu](jsmucny@ucdavis.edu)  \nRECEIVED 30 October 2024  \nACCEPTED 18 December 2024  \nPUBLISHED 15 January 2025  \nCITATION  \nSmucny J, Cannon TD, Bearden CE, Addington J, Cadenhead KS, Cornblatt BA, Keshavan M, Mathalon DH, Perkins DO, Stone W, Walker EF, Woods SW, Davidson I and Carter CS (2025) Predicting conversion to psychosis using machine learning: response to Cannon.  \nFront. Psychiatry 15:1520173 .  \ndoi: 10.3389/fpsyt.2024.1520173  \nCOPYRIGHT  \n© 2025 Smucny, Cannon, Bearden, Addington, Cadenhead, Cornblatt, Keshavan, Mathalon, Perkins, Stone, Walker, Woods, Davidson and Carter. 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.  \nPredicting conversion to psychosis using machine learning: response to Cannon  \nJason Smucny 1*, Tyrone D. Cannon 2,3, Carrie E. Bearden 4,5, Jean Addington 6, Kristen S. Cadenhead 7, Barbara A. Cornblatt 8, Matcheri Keshavan 9, Daniel H. Mathalon 10, Diana O. Perkins 11, William Stone 9, Elaine F. Walker 12, Scott W. Woods 2,3, Ian Davidson 13 and Cameron S. Carter 14  \n1 Department of Psychiatry, University of California, Davis, Davis, CA, United States, 2 Department of Psychology, Yale University, New Haven, CT, United States, 3 Department of Psychiatry, Yale University, New Haven, CT, United States, 4 Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States, 5 Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States, 6 Department of Psychiatry, University of Calgary, Calgary, AB, Canada, 7 Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC, United States, 8 Department of Psychiatry Research, Zucker Hillside Hospital, New York, NY, United States, 9 Department of Psychiatry, Harvard University, Cambridge, MA, United States, 10 Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States, 11 Department of Psychiatry, University of San Diego, San Diego, CA, United States, 12 Department of Psychiatry, Emory University, Atlanta, GA, United States, 13 Department of Computer Science, University of California, Davis, Davis, CA, United States, 14 Department of Psychiatry, University of California, Irvine, Irvine, CA, United States  \nBackground: We previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model that was trained on the NAPLS-3 data, however, requires further suppo","cbCaijgQAIGP6BG7","https://ap.wps.com/l/cbCaijgQAIGP6BG7","pdf",439783,1,7,"English","en",105,"# Background\n## Method\n## Results\n## Conclusion","[{\"question\":\"Why was model generalization tested on NAPLS-2 in this study?\",\"answer\":\"A definitive validation of a model trained on NAPLS-3 requires support in an independent dataset, so the authors evaluated generalization using NAPLS-2 with the same algorithms.\"},{\"question\":\"What approach was used in the method?\",\"answer\":\"Standard machine learning algorithms were trained to predict conversion to psychosis on NAPLS-3 and tested on NAPLS-2.\"},{\"question\":\"How did the model performance on NAPLS-2 compare with earlier results on NAPLS-3?\",\"answer\":\"All models performed above chance, and Naive Bayes and random forest were best, but overall performance on NAPLS-2 did not match prior levels and was not sufficient for clinical application.\"}]","Predicting conversion to psychosis using machine learning: response to Cannon | 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