[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125409-en":3,"doc-seo-125409-105":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},125409,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery","Study aims to identify critical risk factors for acute kidney injury (AKI) after cardiac surgery by combining MIMIC-IV patient data with machine learning and large language models (LLMs). ICU data were used to select predictive features through Lasso regression and random forest, with performance assessed by 10-fold cross-validation using accuracy, sensitivity, specificity, and AUC. LLMs simulated cardiology and nephrology expert judgment to enhance clinical relevance and were validated through clinical expert discussions, yielding 18 key postoperative AKI predictors.","TYPE Original Research PUBLISHED 06 November 2025 DOI 10.3389/fmed.2025.1618222  \nOPEN ACCESS  \nEDITED BY  \nRajendra Bhimma,  \nUniversity of KwaZulu-Natal, South Africa  \nREVIEWED BY  \nVikram Sabapathy,  \nUniversity of Virginia, United States Masao Iwagami,  \nUniversity of Tsukuba, Japan  \n*CORRESPONDENCE  \nTao Liu  \n [liutaocreate@gmail.com](liutaocreate@gmail.com)  \nRECEIVED 25 April 2025  \nACCEPTED 23 September 2025  \nPUBLISHED 06 November 2025  \nCITATION  \nLi Z, Wang L, Zhang X, Wu A and Liu T (2025) Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery.  \nFront. Med. 12:1618222 .  \ndoi: 10.3389/fmed.2025.1618222  \nCOPYRIGHT  \n© 2025 Li, Wang, Zhang, Wu and Liu. This isan 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.  \nIntegration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery  \nZishan Li1 , Lei Wang2 , Xunying Zhang1,3 , Aiping Wu4 and Tao Liu1*  \n1 College of Science, North China University of Science and Technology, Tangshan, China,  \n2 Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, China, 3 College of Automotive Engineering, Hebei College of Science and Technology, Tangshan, China, 4 School of Basic Medical Sciences, North China University of Science and Technology, Tangshan, China  \nObjectives: This study aimed to identify critical risk factors for acute kidney injury (AKI) following cardiac surgery. By integrating patient data from the MIMIC-IV database with large language models (LLMs) and machine learning algorithms, we ensured the clinical relevance of the selected risk factors, providing robust insights for the early identification and intervention of postoperative AKI.  \nMethods: Intensive care unit (ICU) data of patients from the MIMIC-IV database undergoing cardiac surgery were analyzed. Lasso regression and random forest algorithms were used to select significant predictive features from high-dimensional data. Model evaluation involved 10-fold cross-validation and metrics including accuracy, sensitivity, specificity, and the area under the curve. To enhance clinical relevance, LLMs-simulated expert judgment in cardiology and nephrology, which was further validated through discussions with clinical experts.  \nResults: In the cohort consisting of 4,565 patients, a total of 113 important and shared risk factors for AKI were identified, including variables such as anion gap, arterial partial pressure of oxygen (PaO 2), and fraction of inspired oxygen (FiO 2) . Among these, 18 key variables were identified as postoperative AKI predictors via machine learning and LLMs-simulated expert validation. These included anchor age, Creatinine (serum), BUN (Blood Urea Nitrogen), Potassium (serum), Sodium (serum), Lactic Acid, Troponin-T, Furosemide (Lasix), Vancomycin (Random), Gentamicin (Trough), Albumin 5%, ART BP Mean, Cardiac Output (thermodilution), Brain Natriuretic Peptide (BNP), Absolute Count - Lymphs, Absolute Count - Monos, and Absolute Count - Neuts. The integration of LLMs with machine learning algorithms proved effective in accurately identifying clinically relevant risk factors.  \nConclusion: The proposed risk prediction approach for postoperative AKI following cardiac surgery, based on the collaborative analysis of machine learning and large language models (LLMs), effectively identified and validated key clinical risk factors. By simulating expert clinical reasoning, the LLMs significantly e","cbCaignaMIkQzvSf","https://ap.wps.com/l/cbCaignaMIkQzvSf","pdf",2341682,1,13,"English","en",105,"# Objectives\n# Methods\n## Data source and study population\n## Feature selection and evaluation\n## LLM-simulated expert validation\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What is the primary objective of this study?\",\"answer\":\"To identify critical risk factors for acute kidney injury following cardiac surgery using patient data combined with machine learning and large language models.\"},{\"question\":\"How were predictive features selected and evaluated?\",\"answer\":\"Lasso regression and random forest selected significant features from high-dimensional ICU data, and model performance was measured with 10-fold cross-validation using accuracy, sensitivity, specificity, and AUC.\"},{\"question\":\"How did large language models improve clinical relevance in the workflow?\",\"answer\":\"LLMs simulated expert judgment in cardiology and nephrology to support feature selection, and the resulting key predictors were further validated through discussions with clinical experts.\"}]","Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery | PDF",1785898750,33,{"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},"integration-of-machine-learning-and-large-language-models-for-screening-and-identifying-key-risk-factors-of-acute-kidney-injury-after-cardiac-surgery","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integration-of-machine-learning-and-large-language-models-for-screening-and-identifying-key-risk-factors-of-acute-kidney-injury-after-cardiac-surgery/125409/",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 is the primary objective of this study?","Question",{"text":75,"@type":76},"To identify critical risk factors for acute kidney injury following cardiac surgery using patient data combined with machine learning and large language models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were predictive features selected and evaluated?",{"text":80,"@type":76},"Lasso regression and random forest selected significant features from high-dimensional ICU data, and model performance was measured with 10-fold cross-validation using accuracy, sensitivity, specificity, and AUC.",{"name":82,"@type":73,"acceptedAnswer":83},"How did large language models improve clinical relevance in the workflow?",{"text":84,"@type":76},"LLMs simulated expert judgment in cardiology and nephrology to support feature selection, and the resulting key predictors were further validated through discussions with clinical experts.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]