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This study developed and evaluated a machine learning model to predict postoperative AKI risk. Clinical records of 2,025 neonates were randomly split into training and test sets, and six algorithms using 25 predictive variables were compared by ROC AUC and interpreted with SHAP.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-machine-learning-based-model-for-predicting-the-postoperative-risk-of-acute-kidney-injury-in-neonates/441831/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-machine-learning-based-model-for-predicting-the-postoperative-risk-of-acute-kidney-injury-in-neonates/441831.png","ImageObject",300,407,{"name":92,"@type":93},"Chumphorn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the goal of this study?","Question",{"text":112,"@type":113},"To develop and evaluate a machine learning model that predicts postoperative AKI risk in neonates.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the machine learning models evaluated?",{"text":117,"@type":113},"Models were trained on a training set and assessed on a test set using ROC curve area under the curve (AUC), with the best-AUC model selected for validation.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors were most important for predicting postoperative AKI?",{"text":121,"@type":113},"Patient age, operation duration, and urine output were identified as the three most important predictors.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},441831,1790774707,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41,"preview_page_count":4},2336475401981,"https://ap-avatar.wpscdn.com/avatar/22000c94efd8d5204d?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786935347598174694","A machine learning-based model for predicting the postoperative risk of acute kidney injury in neonates  \nLiping He1,2, Tianyin Gao1,2, Yanli Tang1,2, Saifen Jin2, Manli Zhuang1  \n1Department of Operation Room, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; 2Department of Operation Room, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China  \nContributions: (I) Conception and design: M Zhuang; (II) Administrative support: Y Tang; (III) Provision of study materials or patients: S Jin; (IV) Collection and assembly of data: T Gao; (V) Data analysis and interpretation: L He; (VI) Manuscript writing: All authors; (VII) Final approval of  \nmanuscript: All authors.  \nCorrespondence to: Manli Zhuang, MD. Department of Operation Room, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, No. 63 Duobao Road, Guangzhou 510150, China. Email: [zhuangmanli0611@163.com](zhuangmanli0611@163.com).  \nBackground: Acute kidney injury (AKI) is a serious postoperative complication in hospitalized neonates.  \nWe aimed to develop and evaluate a machine learning (ML) model for predicting the risk of postoperative  \nAKI in neonates.  \nMethods: The clinical records of 2,025 neonates were collected, and the patients were randomly divided into training and test sets. The outcome variable was the occurrence of postoperative AKI, and the models incorporated 25 predictive variables, including demographics, intraoperative infusions, and postoperative indicators. ML models were developed using six different algorithms on the training set, and their performance was assessed on the test set using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The model with the best AUC was selected for validation in the test set. The association between the risk factors and postoperative AKI was interpreted using the SHapley Additive  \nexPlanations (SHAP) method.  \nResults: A total of 110 neonatal patients (5.43%) developed AKI following surgery. Patient age, operation duration, and urine output were the three most important predictors of AKI. Among the tested models, the logistic regression (LR) algorithm was the best predictor of postoperative AKI, achieving the highest AUC [median, 0.807; 95% confidence interval (CI): 0.701–0.897] and the highest sensitivity (median, 0.733;  \n95% CI: 0.5–0.938) . The SHAP method was used to illustrate the prediction process of the LR model for  \nneonatal postoperative AKI at the level of individual patients.  \nConclusions: The ML model that uses the LR algorithm with eight commonly measured variables could  \nserve as a tool to predict postoperative AKI in neonates.  \nKeywords: Acute kidney injury (AKI); machine learning (ML); postoperative AKI complications; support vector  \nmachines (SVMs)  \nSubmitted Jun 26, 2025. Accepted for publication Nov 06, 2025. Published online Dec 26, 2025.  \ndoi: 10.21037/tp-2025-428  \nView this article at: [https://dx.doi.org/10.21037/tp-2025-428](https://dx.doi.org/10.21037/tp-2025-428)  \nIntroduction  \nAcute kidney injury (AKI) is characterized by a rapid decline in renal filtration, accompanied by a sudden increase in serum creatinine or a decrease in urine output (1) . AKI  \naffects approximately 13 million hospitalized patients worldwide each year and contributes to nearly two million deaths annually (1) . A meta-analysis of hospitalized children reported that the overall incidence of AKI was 26% and the  \n© AME Publishing Company. Transl Pediatr 2025;14(12):3349-3360 | [https://dx.doi.org/10.21037/tp-2025-428](https://dx.doi.org/10.21037/tp-2025-428)  \n3350 He et al. Postoperative AKI in neonates  \nincidence ","cbCaimUf7qSgUeq1","https://ap.wps.com/l/cbCaimUf7qSgUeq1","pdf",480101,12,"English","# Introduction\n## Methods\n## Results\n## Conclusions\n## Key findings\n## What is known, and what is new?","[{\"question\":\"What was the goal of this study?\",\"answer\":\"To develop and evaluate a machine learning model that predicts postoperative AKI risk in neonates.\"},{\"question\":\"How were the machine learning models evaluated?\",\"answer\":\"Models were trained on a training set and assessed on a test set using ROC curve area under the curve (AUC), with the best-AUC model selected for validation.\"},{\"question\":\"Which factors were most important for predicting postoperative AKI?\",\"answer\":\"Patient age, operation duration, and urine output were identified as the three most important predictors.\"}]","A machine learning-based model for predicting the postoperative risk of acute kidney injury in neonates | PDF",1790697712]