[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124625-en":3,"doc-seo-124625-105":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},124625,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning","Phenotyping analysis with time course supports understanding mechanisms and clinical management of postoperative delirium, yet postoperative delirium has not been fully phenotyped. This study uses hypothesis-free, data-driven symptom categorization with minimal prior knowledge to explore phenotypes after invasive cancer surgery. Patients completed five consecutive days of delirium assessments using DRS-R-98. K-means clustering grouped dimensional symptom scores into grouped features and then multiple delirium-symptom clusters.","TYPE Original Research PUBLISHED 27 June 2023  \nDOI 10.3389/fpsyt.2023.1205605  \nOPEN ACCESS  \nEDITED BY  \nMaya Bizri,  \nAmerican University of Beirut, Lebanon  \nREVIEWED BY  \nRam J. Bishnoi,  \nUniversity of South Florida, United States Semra Bulbuloglu,  \nIstanbul Aydın University, Türkiye  \n*CORRESPONDENCE  \nJunichiro Yoshimoto  \n [junichiro.yoshimoto@fujita-hu.ac.jp](junichiro.yoshimoto@fujita-hu.ac.jp)[ ](junichiro.yoshimoto@fujita-hu.ac.jp)Ryoichi Sadahiro  \n [rsadahir@ncc.go.jp](rsadahir@ncc.go.jp)  \n†These authors have contributed equally to this work  \nRECEIVED 14 April 2023  \nACCEPTED 08 June 2023  \nPUBLISHED 27 June 2023  \nCITATION  \nSri-iesaranusorn P, Sadahiro R, Murakami S, Wada S, Shimizu K, Yoshida T, Aoki K, Uezono Y, Matsuoka H, Ikeda K and Yoshimoto J (2023) Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.  \nFront. Psychiatry 14:1205605 .  \ndoi: 10.3389/fpsyt.2023.1205605  \nCOPYRIGHT  \n© 2023 Sri-iesaranusorn, Sadahiro, Murakami, Wada, Shimizu, Yoshida, Aoki, Uezono, Matsuoka, Ikeda and Yoshimoto. 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.  \nData-driven categorization of postoperative delirium symptoms using unsupervised machine learning  \nPanyawut Sri-iesaranusorn 1†, Ryoichi Sadahiro 2, 3*†, Syo Murakami3, Saho Wada3, 4, Ken Shimizu 5, Teruhiko Yoshida 6, Kazunori Aoki 2, Yasuhito Uezono7, Hiromichi Matsuoka3, Kazushi Ikeda 1 and Junichiro Yoshimoto 1, 8*  \n1 Division of Information Science, Nara Institute of Science and Technology, Nara, Japan, 2 Department of Immune Medicine, National Cancer Center Research Institute, Tokyo, Japan, 3 Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan, 4 Department of Neuropsychiatry, Nippon Medical School, Tama Nagayama Hospital, Tokyo, Japan, 5 Department of Psycho-Oncology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan, 6 Department of Clinical Genomics, National Cancer Center Research Institute, Tokyo, Japan, 7 Department of Pain Control Research, The Jikei University School of Medicine, Tokyo, Japan, 8 Department of Biomedical Data Science, Fujita Health University School of Medicine, Aichi, Japan  \nBackground: Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.  \nMethods: We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day×5days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.  \nResults: Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom f","cbCaitRAKioF5WGI","https://ap.wps.com/l/cbCaitRAKioF5WGI","pdf",3324623,1,10,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What was the study aim regarding postoperative delirium?\",\"answer\":\"To explore phenotypes of postoperative delirium after invasive cancer surgery using a data-driven approach with minimal prior knowledge.\"},{\"question\":\"How were delirium symptoms assessed in the study?\",\"answer\":\"Participants completed five consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale.\"},{\"question\":\"Which machine learning method was used to categorize symptoms?\",\"answer\":\"K-means clustering was applied to dimensional DRS-R-98 scores, first to derive grouped symptom features and then to delineate symptom clusters.\"}]","Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning | PDF",1785893387,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"data-driven-categorization-of-postoperative-delirium-symptoms-using-unsupervised-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"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":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/data-driven-categorization-of-postoperative-delirium-symptoms-using-unsupervised-machine-learning/124625/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the study aim regarding postoperative delirium?","Question",{"text":75,"@type":76},"To explore phenotypes of postoperative delirium after invasive cancer surgery using a data-driven approach with minimal prior knowledge.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were delirium symptoms assessed in the study?",{"text":80,"@type":76},"Participants completed five consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method was used to categorize symptoms?",{"text":84,"@type":76},"K-means clustering was applied to dimensional DRS-R-98 scores, first to derive grouped symptom features and then to delineate symptom clusters.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]