[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123590-en":3,"doc-seo-123590-105":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},123590,1099513958762,"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 for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization - Original Research","Sepsis-associated acute kidney injury (S-AKI) is linked to substantial morbidity and mortality, and existing mortality prediction approaches require improved accuracy for clinical decision-making. A machine learning study analyzed 16,154 S-AKI hospital patients from the MIMIC-IV database, using 129 collected variables to train, validate, and select the best-performing model. Recursive feature elimination identified 15 key predictors, and SHAP-based interpretation supported clinician-facing explainability, with external validation using two hospitals in China.","TYPE Original Research PUBLISHED 03 April 2023  \nDOI 10.3389/fimmu.2023.1140755  \nOPEN ACCESS  \nEDITED BY  \nRahul Kashyap,  \nWellSpan Health, United States  \nREVIEWED BY  \nPratikkumar Vekaria,  \nUniversity of South Carolina, United States Pranjal Sharma,  \nNortheast Ohio Medical University, United States  \n*CORRESPONDENCE Fang Liu  \n [xyliufang@csu.edu.cn](xyliufang@csu.edu.cn)[ ](xyliufang@csu.edu.cn)Li Li  \n [llicu@qq.com](llicu@qq.com)[ ](llicu@qq.com)Qiongjing Yuan  \n [yuanqiongjing@csu.edu.cn](yuanqiongjing@csu.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nSPECIALTY SECTION  \nThis article was submitted to Systems Immunology, a section of the journal Frontiers in Immunology  \nRECEIVED 09 January 2023  \nACCEPTED 17 March 2023  \nPUBLISHED 03 April 2023  \nCITATION  \nZhou H, Liu L, Zhao Q, Jin X, Peng Z, Wang W, Huang L, Xie Y, Xu H, Tao L, Xiao X, Nie W, Liu F, Li L and Yuan Q (2023)  \nMachine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.  \nFront. Immunol. 14:1140755 .  \ndoi: 10.3389/fimmu.2023.1140755  \nCOPYRIGHT  \n© 2023 Zhou, Liu, Zhao, Jin, Peng, Wang, Huang, Xie, Xu, Tao, Xiao, Nie, Liu, Li and Yuan. 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 for the prediction of all-cause mortality in patients with sepsisassociated acute kidney injury during hospitalization  \nHongshan Zhou 1†, Leping Liu 2†, Qinyu Zhao 3†, Xin Jin 4, Zhangzhe Peng 1,5,6, Wei Wang 1,5,6, Ling Huang 1,5,6, Yanyun Xie 1,5,6, Hui Xu 1, Lijian Tao 1,5,6, Xiangcheng Xiao 1, Wannian Nie 1, Fang Liu 7*, Li Li 8* and Qiongjing Yuan 1,5,6,9*  \n1 Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China,  \n2 Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, China, 3College of Engineering and Computer Science, Australian National University, Canberra, ACT, Australia, 4Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China, 5Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China, 6 National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China, 7Health Management Center, Xiangya Hospital of Central South University, Changsha, Hunan, China, 8Critical Care Medicine, Xiangya Hospital of Central South University, Changsha, Hunan, China, 9 National Clinical Medical Research Center for Geriatric Diseases, Xiangya Hospital of Central South University, Changsha, Hunan, China  \nBackground: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU) .  \nMethods: A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%) . Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed ","cbCaiqNE847sGuMb","https://ap.wps.com/l/cbCaiqNE847sGuMb","pdf",2031666,1,10,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Keywords","[{\"question\":\"What problem does the study address in sepsis-associated acute kidney injury (S-AKI) patients?\",\"answer\":\"The study targets the high morbidity and mortality of S-AKI and aims to predict all-cause in-hospital death by identifying key variables linked to mortality and building a machine learning risk model.\"},{\"question\":\"How was the machine learning model developed and validated?\",\"answer\":\"A total of 16,154 S-AKI patients from the MIMIC-IV database were split into training (80%) and validation (20%) sets. The researchers evaluated 11 algorithms, used recursive feature elimination to select important variables, and then performed external validation using data from two hospitals in China.\"},{\"question\":\"Which variables were selected and what model performed best?\",\"answer\":\"The final model used 15 critical predictors, including urine output, maximum blood urea nitrogen, creatinine measures, heart rate and respiratory variables, temperature, inspired oxygen fraction, and diagnoses such as diabetes and stroke. The CatBoost categorical boosting algorithm showed the best predictive performance.\"}]","Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization - Original Research | PDF",1785817519,25,{"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},"machine-learning-for-the-prediction-of-all-cause-mortality-in-patients-with-sepsis-associated-acute-kidney-injury-during-hospitalization-original-research","",{"@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/machine-learning-for-the-prediction-of-all-cause-mortality-in-patients-with-sepsis-associated-acute-kidney-injury-during-hospitalization-original-research/123590/",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","2026-08-04",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 problem does the study address in sepsis-associated acute kidney injury (S-AKI) patients?","Question",{"text":75,"@type":76},"The study targets the high morbidity and mortality of S-AKI and aims to predict all-cause in-hospital death by identifying key variables linked to mortality and building a machine learning risk model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model developed and validated?",{"text":80,"@type":76},"A total of 16,154 S-AKI patients from the MIMIC-IV database were split into training (80%) and validation (20%) sets. The researchers evaluated 11 algorithms, used recursive feature elimination to select important variables, and then performed external validation using data from two hospitals in China.",{"name":82,"@type":73,"acceptedAnswer":83},"Which variables were selected and what model performed best?",{"text":84,"@type":76},"The final model used 15 critical predictors, including urine output, maximum blood urea nitrogen, creatinine measures, heart rate and respiratory variables, temperature, inspired oxygen fraction, and diagnoses such as diabetes and stroke. 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