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Neurologist-authored narratives from 21 spoke hospitals (2,887 suspected cases; 1,060 in final dataset) were processed via NLP embeddings and supervised ML, using contextual LLMs and five algorithms with class-imbalance mitigation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/natural-language-processing-for-triage-of-cerebral-large-vessel-occlusion/438568/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/natural-language-processing-for-triage-of-cerebral-large-vessel-occlusion/438568.png","ImageObject",300,407,{"name":42,"@type":43},"Patrick","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-01","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the study address in stroke triage?","Question",{"text":63,"@type":64},"The study targets timely detection of cerebral large-vessel occlusion (LVO) in ischemic stroke to support prehospital triage and faster thrombectomy-team mobilization.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What data and modeling approach were used?",{"text":68,"@type":64},"Neurologist-authored free-text case summaries from a national TeleStroke network were analyzed using NLP (text embedding) and supervised machine learning, evaluating contextual LLMs with multiple algorithms.",{"name":70,"@type":61,"acceptedAnswer":71},"What were the key performance outcomes for detecting LVO?",{"text":72,"@type":64},"For 1,060 final cases (143 with confirmed proximal occlusions), AdaBoost paired with BioBERT achieved the highest accuracy (89.82%), precision (98.37%), and AUC (89.86%). BioBERT consistently outperformed other models, and adding NIHSS as a feature improved recall.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},438568,1790740442,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":116,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":144,"preview_page_count":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","Article published online: 2025-12-02  \nOriginal Article 1  \nNatural language processing for triage of cerebral large-vessel occlusion  \nJoão Brainer Clares de Andrade 1,2,3,4 José Marcio Duarte 1 Thales Pardini Fagundes5  \nThiago Bulhões da Silva Costa 1 Paulo B. Paiva2 André Shimaoka2 Antonio C. da Silva Junior2 Evelyn de Paula Pacheco6 Sophia Oliveira Querobin7 Marialdo Augusto Cordeiro de Souza Junior7 Eduardo Saucedo Lage6 Gisele Sampaio Silva2,3  \n1 Universidade Federal de São Paulo, Departamento de Informática em Saúde, São Paulo SP, Brazil.  \n2 Universidade Federal de São Paulo, Departamento de Neurologia, São Paulo SP, Brazil.  \n3 Hospital Israelita Albert Einstein, Centro de Ensino e Pesquisa Albert Einstein, São Paulo SP, Brazil.  \n4 Instituto Tecnológico de Aeronáutica, São José dos Campos SP, Brazil.  \n5 Hospital de Amor de Barretos, Serviço de Neurologia, Barretos SP, Brazil.  \n6 AMIL Sistema de Saúde, Serviço de Teleneurologia, São Paulo SP, Brazil.  \n7 Centro Universitário São Camilo, Faculdade de Medicina, São Paulo SP, Brazil.  \nArq. Neuro-Psiquiatr. 2025;83(11):s00451813238 .  \nAddress for correspondence João Brainer Clares de Andrade (email: joao.brainer@unifesp.br)  \nAbstract  \nKeywords  \n► Artificial Intelligence  \n► Mass Screening  \n► Natural Language Processing  \n► Stroke  \nBackground Timely identification of large-vessel occlusion (LVO) in ischemic stroke is essential for optimizing prehospital triage and enabling rapid mobilization of thrombectomy-capable teams. Traditional LVO screening tools are often lengthy and reliant on neurological examination skills that may be inaccessible to nonspecialists. Objective To assess the ability of large language models (LLMs) to detect LVO using only free-text summaries, with or without National Institutes of Health Stroke Scale (NIHSS) data, in a national teleneurology service.  \nMethods We conducted a retrospective analysis of 2,887 suspected stroke cases across 21 spoke hospitals within a national TeleStroke network. Neurologist-authored case summaries were processed using natural language processing techniques, including text embedding and supervised machine learning classification. Contextual LLMs (BERTimbau, BioBERTpt, GPorTuguese-2) were evaluated with five algorithms. The Bootstrap method was employed to mitigate class imbalance, with performance averaging over 100 iterations.  \nResults Of 1,060 cases included in the final dataset, 143 had confirmed proximal occlusions. Median Alberta Stroke Program Early CT Score (ASPECTS) was 9 and mean National Institutes of Health Stroke Scale (NIHSS) was 5.4 􀀁 2. AdaBoost paired with BioBERT yielded the highest accuracy (89.82%), precision (98.37%), and AUC (89.86%) . Incorporating NIHSS as a numerical feature improved recall (87.60% with multilayer  \nreceived  \nApril 23, 2025  \nreceived in its final form September 1, 2025 accepted  \nSeptember 13, 2025  \nDOI [https://doi.org/](https://doi.org/)[ ](https://doi.org/)10.1055/s-0045-1813238 . ISSN 0004-282X.  \nEditor-in-Chief: Ayrton Roberto Massaro.  \nAssociate Editor: Diana Aguiar de Sousa.  \n© 2025 . The Author(s) .  \nThis is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))  \nThieme Revinter Publicações Ltda. , Rua Rego Freitas, 175, loja 1, República, São Paulo, SP, CEP 01220-010, Brazil  \n2 Natural language processing for stroke triage Andrade et al.  \nperceptron) and F1-score (89.05% with Dense Neural Network) . BioBERT consistently outperformed other models, regardless of NIHSS inclusion.  \nConclusion The LLM-based models demonstrated strong performance in identifying LVO using routine clinical narratives. These findings support the integration of NLP and ML inTeleStroke systems and underscore the need for further va","cbCaisc2smFCZen8","https://ap.wps.com/l/cbCaisc2smFCZen8","pdf",953840,"English","# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does the study address in stroke triage?\",\"answer\":\"The study targets timely detection of cerebral large-vessel occlusion (LVO) in ischemic stroke to support prehospital triage and faster thrombectomy-team mobilization.\"},{\"question\":\"What data and modeling approach were used?\",\"answer\":\"Neurologist-authored free-text case summaries from a national TeleStroke network were analyzed using NLP (text embedding) and supervised machine learning, evaluating contextual LLMs with multiple algorithms.\"},{\"question\":\"What were the key performance outcomes for detecting LVO?\",\"answer\":\"For 1,060 final cases (143 with confirmed proximal occlusions), AdaBoost paired with BioBERT achieved the highest accuracy (89.82%), precision (98.37%), and AUC (89.86%). BioBERT consistently outperformed other models, and adding NIHSS as a feature improved recall.\"}]","Natural language processing for triage of cerebral large-vessel occlusion | PDF",1790685733,23]