[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120361-en":3,"doc-seo-120361-105":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":20,"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},120361,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin","Acute kidney injury represents a major clinical challenge in hospitalized patients treated with aminoglycosides. This study retrospectively analyzed 7,028 inpatients given amikacin or etimicin between 2018 and 2020 to screen AKI risk factors and build machine learning prediction models. Univariate methods and least absolute shrinkage and selection operator (LASSO) supported feature screening. Multiple algorithms were evaluated, with XGBoost performing best for amikacin-associated AKI and GBM best for etimicin-treated patients.","TYPE Original Research PUBLISHED 22 May 2025  \nDOI 10.3389/fphar.2025.1538074  \nOPEN ACCESS  \nEDITED BY  \nYoshiaki Uyama,  \nPharmaceuticals and Medical Devices Agency, Japan  \nREVIEWED BY  \nShuhe Li,  \nUniversity of Exeter, United Kingdom Masao Iwagami,  \nUniversity of Tsukuba, Japan  \n*CORRESPONDENCE  \nXiao Li,  \n [lixiao1688@163.com](lixiao1688@163.com),  \n [x.li@sdu.edu.cn](x.li@sdu.edu.cn)[ ](x.li@sdu.edu.cn)Xin Huang,  \n [13791120711@126.com](13791120711@126.com)  \n†These authors have contributed equally to this work  \nRECEIVED 02 December 2024  \nACCEPTED 12 May 2025  \nPUBLISHED 22 May 2025  \nCITATION  \nZhang P, Chen Q, Lao J, Shi J, Cao J, Li X and Huang X (2025) Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin.  \nFront. Pharmacol. 16:1538074 .  \ndoi: 10.3389/fphar.2025.1538074  \nCOPYRIGHT  \n© 2025 Zhang, Chen, Lao, Shi, Cao, Li and Huang. 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 modeling for the risk of acute kidney injury in inpatients receiving amikacin andetimicin  \nPei Zhang 1†, Qiong Chen 2†, Jiahui Lao 3, Juan Shi 4, Jia Cao 3, Xiao Li 1* and Xin Huang 1*  \n1Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China, 2Department of Dermatology, The First People’s Hospital of Jinan, Jinan, China, 3Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China, 4Department of Clinical Pharmacy, The First People ’s Hospital of Jinan, Jinan, China  \nBackground: Acute kidney injury (AKI) is a significant concern among hospitalized patients receiving aminoglycosides. Identifying the risk factors associated with aminoglycoside-induced AKI and developing machine learning models are imperative in clinical practice.  \nObjective: This study aims to identify the risk factors associated with AKI in hospitalized patients receiving aminoglycosides, and develop machine learning models for evaluation of the AKI risk in these patients.  \nMethods: This study retrospectively analyzed 7,028 hospitalized patients who received treatment with amikacin or etimicin between 2018 and 2020. According to the type of medication used, patients were divided into amikacin group (n = 307) and etimicin group (n = 6,901) . Univariate analyses and the least absolute shrinkage and selection operator algorithm were used to screen risk factors and construct the model. The machine learning models were developed using five different algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting model (XGBoost), and light gradient boosting machine (Light GBM) .  \nResults: The XGBoost model exhibited the most superior performance in predicting amikacin-associated AKI among the developed machine learning models. For the training set, the area under the receiver-operator characteristic curve (AUC) was 0 . 916, and for the test set, it was 0 .841. The model can be accessed online. Regarding AKI risk in etimicin-treated patients, the GBM model demonstrated the best overall performance, with AUC values of 0. 886 for the training set and 0 . 900 for the test set. The model was also made available online.  \nConclusion: These predictive models may offer a valuable tool for est","cbCailNKaDVCzgFW","https://ap.wps.com/l/cbCailNKaDVCzgFW","pdf",1639927,1,16,"English","en",105,"# Background\n## Study objective\n# Methods\n## Study design and population\n## Model development\n# Results\n## Amikacin-associated AKI prediction\n## Etimicin-treated AKI prediction\n# Conclusion","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"It targets the risk of acute kidney injury (AKI) among hospitalized patients receiving aminoglycosides, specifically amikacin and etimicin.\"},{\"question\":\"How was the study designed and what data were used?\",\"answer\":\"It used a retrospective analysis of 7,028 hospitalized patients treated with amikacin or etimicin from 2018 to 2020.\"},{\"question\":\"Which machine learning models performed best for each medication group?\",\"answer\":\"For amikacin-associated AKI, the XGBoost model showed the best performance; for etimicin-treated patients, the GBM model demonstrated the best overall performance.\"}]","Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin | PDF",1785729664,40,{"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-modeling-for-the-risk-of-acute-kidney-injury-in-inpatients-receiving-amikacin-and-etimicin","",{"@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-modeling-for-the-risk-of-acute-kidney-injury-in-inpatients-receiving-amikacin-and-etimicin/120361/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study address?","Question",{"text":75,"@type":76},"It targets the risk of acute kidney injury (AKI) among hospitalized patients receiving aminoglycosides, specifically amikacin and etimicin.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the study designed and what data were used?",{"text":80,"@type":76},"It used a retrospective analysis of 7,028 hospitalized patients treated with amikacin or etimicin from 2018 to 2020.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models performed best for each medication group?",{"text":84,"@type":76},"For amikacin-associated AKI, the XGBoost model showed the best performance; 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