[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126370-en":3,"doc-seo-126370-105":30,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":20,"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},126370,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Phase-specific kidney graft failure prediction with machine learning model - Research","Accurate prediction of kidney graft failure across distinct post-transplant phases is essential for timely intervention and long-term allograft preservation. This study developed dynamically evaluated, phase-specific machine learning models to estimate risk over five intervals (0–3, 3–9, 9–15, 15–39, and 39–72 months). Training and internal validation used retrospective data from deceased donor kidney transplant recipients, with additional blinded external validation. Performance was quantified using ROC AUC, F1 score, and G-mean.","TYPE Original Research PUBLISHED 02 October 2025 DOI 10.3389/frai.2025.1682639  \nOPEN ACCESS  \nEDITED BY  \nAntonio Sarasa-Cabezuelo,  \nComplutense University of Madrid, Spain  \nREVIEWED BY  \nBharadhwaj Ravindhran,  \nHull York Medical School, United Kingdom Kiruthika Balakrishnan,  \nUniversity of Illinois Chicago, United States  \n*CORRESPONDENCE  \nAmankeldi A. Salybekov  \n [amansaab0@gmail.com](amansaab0@gmail.com)[ ](amansaab0@gmail.com)Markus Wolfien  \n markus.wolfien@tu-dresden. de RECEIVED 09 August 2025 ACCEPTED 10 September 2025 PUBLISHED 02 October 2025  \nCITATION  \nSalybekov AA, Wolfien M, Yerkos A, Buribayev Z, Hidaka S and Kobayashi S (2025) Phase-specific kidney graft failure prediction with machine learning model.  \nFront. Artif. Intell. 8:1682639.  \ndoi: 10.3389/frai.2025.1682639  \nCOPYRIGHT  \n© 2025 Salybekov, Wolfien, Yerkos, Buribayev, Hidaka and Kobayashi. This is an open-access article distributed under the terms of the  \nCreative 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.  \nPhase-specific kidney graft failure prediction with machine learning model  \nAmankeldi A. Salybekov 1,2*, Markus Wolfien3,4*, Ainur Yerkos 5, Zholdas Buribayev 5, Sumi Hidaka 1 and Shuzo Kobayashi 1  \n1 Kidney Disease and Transplant Center, Shonan Kamakura General Hospital, Kamakura, Japan,  \n2 Regenerative Medicine Division, Cell and Gene Therapy Department, Qazaq Institute of Innovative Medicine, Astana, Kazakhstan, 3 Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, TUD Dresden University of Technology, Dresden, Germany, 4Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany, 5 Department of Computer Science, Al-Farabi Kazakh National University, Almaty, Kazakhstan  \nBackground: Accurate prediction of kidney graft failure at different phases post-transplantation is critical for timely intervention and long-term allograft preservation. Traditional survival models offer limited capacity for dynamic, time-specific risk estimation. Machine learning (ML) approaches, with their ability to model complex patterns, present a promising alternative.  \nMethods: This study developed and dynamically evaluated phase-specific ML models to predict kidney graft failure across five post-transplant intervals: 0–3 months, 3–9 months, 9–15 months, 15–39 months, and 39–72 months. Clinically relevant retrospective data from deceased donor kidney transplant recipients were used for training and internal validation, with performance further confirmed on a blinded external validation cohort. Predictive performance was assessed using ROC AUC, F1 score, and G-mean.  \nResults: The ML models demonstrated varying performance across time intervals. Short-term predictions in the 0–3 month and 3–9 month intervals yielded moderate accuracy (ROC AUC = 0.73 ± 0.07 and 0.72 ± 0.04, respectively) . The highest predictive accuracy observed in mid-term or the 9–15-month window (ROCAUC = 0.92 ± 0.02; F1 score = 0.85 ± 0.03), followed by the 15–39-month period (ROC AUC = 0.84 ± 0.04; F1 score = 0.76 ± 0.04) . Long-term prediction from 39 to 72 months was more challenging (ROC AUC = 0.70 ± 0.07; F1 score = 0.65 ± 0.06) .  \nConclusion: Phase-specific ML models offer robust predictive performance for kidney graft failure, particularly in mid-term periods, supporting their integration into dynamic post-transplant surveillance strategies. These models can aid clinicians in identifying high-risk patients and tailoring follow-up protocols to optimize long-term transplant outcomes.  \nKEYWORDS  \nkidney transplantation, graft failure, machine learning, deceased donor, survival prediction  \n","cbCaichZQw2TUTfT","https://ap.wps.com/l/cbCaichZQw2TUTfT","pdf",1017629,9,1,"English","en",105,"# Introduction\n## Methods\n## Results\n## Conclusion","[{\"question\":\"Why is phase-specific prediction important for kidney transplant graft outcomes?\",\"answer\":\"Because risk and clinical patterns evolve across time after transplantation, enabling timely intervention and better long-term allograft preservation than single static survival models.\"},{\"question\":\"How were the machine learning models evaluated in the study?\",\"answer\":\"Models were trained and internally validated on retrospective deceased donor transplant data, then assessed again using a blinded external validation cohort. Performance used ROC AUC, F1 score, and G-mean.\"},{\"question\":\"Which post-transplant interval showed the highest predictive accuracy?\",\"answer\":\"The mid-term 9–15 month interval achieved the highest accuracy, with ROC AUC around 0.92 and an F1 score around 0.85.\"}]","Phase-specific kidney graft failure prediction with machine learning model - Research | PDF",1785904706,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"phase-specific-kidney-graft-failure-prediction-with-machine-learning-model-research","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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/phase-specific-kidney-graft-failure-prediction-with-machine-learning-model-research/126370/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is phase-specific prediction important for kidney transplant graft outcomes?","Question",{"text":76,"@type":77},"Because risk and clinical patterns evolve across time after transplantation, enabling timely intervention and better long-term allograft preservation than single static survival models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the machine learning models evaluated in the study?",{"text":81,"@type":77},"Models were trained and internally validated on retrospective deceased donor transplant data, then assessed again using a blinded external validation cohort. 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