[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-436095-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-436095-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","using-artificial-intelligence-algorithms-to-predict-the-overall-survival-of-hemodialysis-patients-during-the-covid-19-pandemic-a-prospective-cohort-study","Using Artificial Intelligence Algorithms to Predict the Overall Survival of Hemodialysis Patients During the COVID-19 Pandemic - A Prospective Cohort Study","","Background: Hemodialysis (HD) patients are at high risk for severe COVID-19 outcomes, yet the survival impact of partial COVID-19 vaccination in HD remains uncertain. This prospective cohort study used artificial intelligence algorithms to predict survival effects of partial vaccination. Methods: A cohort of 433 HD patients trained and tested machine-learning models using clinical features collected from July 1, 2021 to April 29, 2022, and evaluated discriminative performance. Results: LightGBM achieved the highest F1 score of 0.95, with key features including age, albumin, and vaccination details. Conclusion: The models help identify high-risk individuals and inform vaccination strategies.",{"@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/using-artificial-intelligence-algorithms-to-predict-the-overall-survival-of-hemodialysis-patients-during-the-covid-19-pandemic-a-prospective-cohort-study/436095/",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/using-artificial-intelligence-algorithms-to-predict-the-overall-survival-of-hemodialysis-patients-during-the-covid-19-pandemic-a-prospective-cohort-study/436095.png","ImageObject",300,407,{"name":42,"@type":43},"Oliver","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-03","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What question does the study address about hemodialysis patients and COVID-19 vaccination?","Question",{"text":63,"@type":64},"The study examines how partial COVID-19 vaccination may affect the overall survival of hemodialysis (HD) patients during the pandemic.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the predictive models developed and evaluated?",{"text":68,"@type":64},"A cohort of 433 HD patients was split into training (80%) and testing (20%) sets, and machine-learning models including CatBoost, LightGBM, RandomForest, and XGBoost were evaluated on their discriminative performance.",{"name":70,"@type":61,"acceptedAnswer":71},"Which model performed best and what factors influenced survival?",{"text":72,"@type":64},"LightGBM achieved the highest F1 score (0.95). The SHapley Additive explanation summary from XGBoost indicated that age, albumin, and vaccination details significantly impacted survival.","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},436095,1790793981,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,119,122,126],{"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":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"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":111,"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":117,"preview_page_count":4},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","J Chin MedAssoc  \nOriginal article  \nUsing artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study  \nShao-Yu Tanga, Tz-Heng Chenb,c,d, Ko-Lin Kuoa, Jue-Ni Huange, Chen-Tsung Kuoe,f,g,* , Yuan-Chia Chue,f,g,*  \naDivision of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC; bDivision of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC; cSchool of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC; dInstitute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC; eInformation Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC; fBig Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC; gDepartment of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC  \nAbstract  \nBackground: Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19 . The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. Methods: A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost) , light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts.  \nResults: Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptorbinding domain antibodies.  \nConclusion: This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.  \nKeywords: Artificial intelligence; COVID-19; Hemodialysis; Predictive modeling; Survival; Vaccination  \n1. INTRODUCTION  \nThe COVID-19 pandemic, which emerged in late 2019, had a profound and far-reaching impact on the global economy and healthcare systems. Among the numerous challenges posed by this ongoing crisis, individuals undergoing chronic hemodialysis (HD) face unique and important difficulties. Patients receiving  \n*Address correspondence Dr. Chen-Tsung Kuo and Dr. Yuan-Chia Chu, Information Management Office, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, [ROC. E-mail address: ctkuo2@vghtpe.gov.tw](ROC. E-mail address: ctkuo2@vghtpe.gov.tw)[ ](ROC. E-mail address: ctkuo2@vghtpe.gov.tw)(C.-T. Kuo); [E-mail address: xd.yuanchia@gmail.com](E-mail address: xd.yuanchia@gmail.com) (Y.-C. Chu).  \nConflicts of interest: The authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article.  \nJournal of Chinese Medical Association. (2023) 86: 1020-1027.  \nRec","cbCaijEFwT9bHuz7","https://ap.wps.com/l/cbCaijEFwT9bHuz7","pdf",986332,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Keywords\n# Introduction","[{\"question\":\"What question does the study address about hemodialysis patients and COVID-19 vaccination?\",\"answer\":\"The study examines how partial COVID-19 vaccination may affect the overall survival of hemodialysis (HD) patients during the pandemic.\"},{\"question\":\"How were the predictive models developed and evaluated?\",\"answer\":\"A cohort of 433 HD patients was split into training (80%) and testing (20%) sets, and machine-learning models including CatBoost, LightGBM, RandomForest, and XGBoost were evaluated on their discriminative performance.\"},{\"question\":\"Which model performed best and what factors influenced survival?\",\"answer\":\"LightGBM achieved the highest F1 score (0.95). The SHapley Additive explanation summary from XGBoost indicated that age, albumin, and vaccination details significantly impacted survival.\"}]","Using Artificial Intelligence Algorithms to Predict the Overall Survival of Hemodialysis Patients During the COVID-19 Pandemic - A Prospective Cohort Study | PDF",1790676765]