[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125563-en":3,"doc-seo-125563-105":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":20,"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},125563,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke","Accurate prediction of individual clinical outcomes after acute stroke is essential for tailoring treatment and planning follow-up care. This study applies advanced machine learning to compare prediction of functional recovery, cognitive performance, depression, and mortality in patients with first-ever ischemic stroke, and to determine principal prognostic factors driving these outcomes. Models are trained on baseline features and evaluated with nested cross-validation to support reliable outcome prediction.","TYPE Original Research PUBLISHED 21 February 2023 DOI 10. 3389/fneur.2023.1114360  \nOPEN ACCESS  \nEDITED BY  \nNishant K. Mishra,  \nYale University, United States  \nREVIEWED BY  \nAmit Mehndiratta,  \nIndian Institute of Technology Delhi, India Shubham Misra,  \nYale University, United States  \n*CORRESPONDENCE  \nKerstin Ritter  \n [kerstin.ritter@charite.de](kerstin.ritter@charite.de)  \nSPECIALTY SECTION  \nThis article was submitted to Stroke,  \na section of the journal Frontiers in Neurology  \nRECEIVED 02 December 2022  \nACCEPTED 31 January 2023  \nPUBLISHED 21 February 2023  \nCITATION  \nFast L, Temuulen U, Villringer K, Kufner A, Ali HF, Siebert E, Huo S, Piper SK, Sperber PS, Liman T, Endres M and Ritter K (2023) Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.  \nFront. Neurol. 14:1114360 .  \ndoi: 10.3389/fneur.2023.1114360  \nCOPYRIGHT  \n© 2023 Fast, Temuulen, Villringer, Kufner, Ali, Siebert, Huo, Piper, Sperber, Liman, Endres and Ritter. 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.  \nMachine learning-based prediction of clinical outcomes after first-ever ischemic stroke  \nLea Fast1 , Uchralt Temuulen2 , Kersten Villringer2 , Anna Kufner2,3,4 ,  \nHuma Fatima Ali5 , Eberhard Siebert6 , Shufan Huo2,4,7 , Sophie K. Piper3,8,9 , Pia Sophie Sperber2,10,11,12 , Thomas Liman2,7,13,14 , Matthias Endres2,3,4,7,10,13 and Kerstin Ritter1,15*  \n1 Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and  \nHumboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Berlin, Germany, 2 Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany, 3 Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany, 4 Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Berlin, Germany, 5 Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany, 6 Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuroradiology, Berlin, Germany, 7 German Center for Cardiovascular Research (Deutsches Zentrum für Herz-Kreislauferkrankungen, DZHK), Partner Site Berlin, Berlin, Germany, 8 Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Berlin, Germany, 9 Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany, 10 Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence, NeuroCure Clinical Research Center (NCRC), Berlin, Germany, 11 Experimental and Clinical Research Center, A Cooperation Between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité – Universitätsmedizin Berlin, Berlin, Germany, 12 Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany, 13 German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen, DZNE), Partner Site Berlin, Berlin, Germany, 14 Department of Neurology, Evangelical Hospital Oldenburg, Carl von Ossietzky-University, Oldenburg, Germany, 15 Charité -Universitätsmedizin Berlin, Corpora","cbCaicMub7iLINvB","https://ap.wps.com/l/cbCaicMub7iLINvB","pdf",2486059,1,14,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What outcomes are predicted for first-ever ischemic stroke patients?\",\"answer\":\"The study predicts functional recovery, cognitive function, depression, and mortality, using clinical outcome measures including mRS, BI, MMSE, TICS-M, CES-D, and survival.\"},{\"question\":\"How were the machine learning models trained and validated?\",\"answer\":\"The models predict outcomes for 307 patients using 43 baseline features and are evaluated using repeated 5-fold nested cross-validation.\"},{\"question\":\"Which prognostic factors were identified as most important?\",\"answer\":\"Shapley additive explanations are used to identify leading prognostic features, with NIHSS as the top predictor for most functional recovery outcomes, and education linked to cognitive function and depression.\"}]","Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke | 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