[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126366-en":3,"doc-seo-126366-105":31,"detail-sidebar-cat-0-en-105":93},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126366,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine learning-based predictive model for the perioperative co-occurrence of T-cell-mediated rejection and pneumonia in liver transplantation - Research findings","A machine learning workflow predicts the perioperative co-occurrence of T-cell-mediated rejection (TCMR) and pneumonia after liver transplantation, addressing how both events can compromise graft function and patient survival. Recipient clinical data were retrospectively collected, key predictors were selected via LASSO regression, and five ML algorithms were trained and evaluated using ROC and calibration curves. The SVM model achieved strong discriminative and calibration performance, with SHAP used to interpret variable contributions and individual risk drivers.","TYPE Original Research PUBLISHED 17 September 2025 DOI 10.3389/fimmu.2025.1648993  \nOPEN ACCESS  \nEDITED BY  \nAntonio Sarasa-Cabezuelo,  \nComplutense University of Madrid, Spain  \nREVIEWED BY  \nXin Xue,  \nSoutheast University, China Jing Yu,  \nGansu Provincial Hospital, China  \n*CORRESPONDENCE  \nXuyong Sun  \n [sunxuyong@gxmu.edu.cn](sunxuyong@gxmu.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 18 June 2025  \nACCEPTED 03 September 2025  \nPUBLISHED 17 September 2025  \nCITATION  \nSun J, Zhu G, Liang Q, Wen N, Li H and Sun X (2025) Machine learning-based predictive model for the perioperative co-occurrence of T-cell-mediated rejection and pneumonia in liver transplantation.  \nFront. Immunol. 16:1648993 .  \ndoi: 10.3389/fimmu.2025.1648993  \nCOPYRIGHT  \n© 2025 Sun, Zhu, Liang, Wen, Li and Sun. 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-based predictive model for the perioperative co-occurrence of T-cell-mediated rejection and pneumonia in liver transplantation  \nJunjie Sun †, Guangyi Zhu †, Qingwen Liang, Ning Wen, Haibin Li and Xuyong Sun*  \nInstitute of Transplant Medicine, The Second Affiliated Hospital of Guangxi Medical University, Guangxi Clinical Research Center for Organ Transplantation, Guangxi Key Laboratory of Organ Donation and Transplantation, Nanning, China  \nObjective: Perioperative T-cell-mediated rejection (TCMR) and pneumonia occurrence significantly impair graft function and patient survival following liver transplantation (LT) . This article aims to develop a machine learning (ML) -based model to predict perioperative co-occurrence of TCMR and pneumonia. Methods: Recipient-related data were retrospectively collected. Predictive Variables were identified through LASSO regression analysis. Five machine learning algorithms, including support vector machine (SVM), were employed to develop predictive models. Model performance was appraised via the receiver operating characteristic (ROC) curve, and calibration curve. SHapley Additive exPlanations (SHAP) method was employed to visualize model characteristics and individual predictions.  \nResults: This study enrolled 717 LT recipients, including 93 patients with perioperative co-occurrence of TCMR and pneumonia. LASSO regression identified postoperative direct bilirubin, postoperative international normalized ratio, high-density lipoprotein, postoperative alanine aminotransferase, natural killer cell, tacrolimus (FK506) concentration, Na+, operative time, anhepatic phase, induction regimen, and ICU stay as significant predictors. The SVM model demonstrated superior predictive performance, with area under the curve values of 0.881 (95% CI: 0.83–0.93) and 0.786 (95% CI: 0.69–0.88) in the training and test sets, respectively. The calibration curve showed high agreement between the predicted and observed risks. The SVM model demonstrated superior specificity, sensitivity, F1 score, and recall compared to other models . SHAP analysis identified variables that contributed to the model predictions.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusions: This study constructed a robust predictive model for the perioperative co-occurrence of TCMR and pneumonia. The SVM model demonstrated superior predictive performance.  \nKEYWORDS  \nmachine learning, liver transplantation, T-cell-mediated rejection, pneumonia, perioperative period, predictive model  \nIntroduction  \nLiver transplantation (LT) has become the optimal treatment for end-stage liver disease. Although liver is considered an immunologi","cbCaieIOIUJ5MvK3","https://ap.wps.com/l/cbCaieIOIUJ5MvK3","pdf",6933815,7,1,15,"English","en",105,"# Objective\n# Methods\n## Data and feature selection\n## Model development and evaluation\n## Interpretability with SHAP\n# Results\n## Cohort and incidence\n## Predictors identified by LASSO\n## Model performance\n# Conclusions\n# Keywords","[{\"question\":\"What does the proposed model predict in liver transplantation?\",\"answer\":\"It predicts the perioperative co-occurrence of T-cell-mediated rejection and pneumonia after liver transplantation.\"},{\"question\":\"How were predictive variables selected for the model?\",\"answer\":\"Predictive variables were identified using LASSO regression analysis from recipient-related retrospective data.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"The SVM model showed superior predictive performance, assessed with ROC curves and calibration curves, and compared on metrics such as specificity, sensitivity, F1 score, and recall.\"}]","Machine learning-based predictive model for the perioperative co-occurrence of T-cell-mediated rejection and pneumonia in liver transplantation - Research findings | PDF",1785904695,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-predictive-model-for-the-perioperative-co-occurrence-of-t-cell-mediated-rejection-and-pneumonia-in-liver-transplantation-research-findings","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-predictive-model-for-the-perioperative-co-occurrence-of-t-cell-mediated-rejection-and-pneumonia-in-liver-transplantation-research-findings/126366/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What does the proposed model predict in liver transplantation?","Question",{"text":77,"@type":78},"It predicts the perioperative co-occurrence of T-cell-mediated rejection and pneumonia after liver transplantation.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were predictive variables selected for the model?",{"text":82,"@type":78},"Predictive variables were identified using LASSO regression analysis from recipient-related retrospective data.",{"name":84,"@type":75,"acceptedAnswer":85},"Which model performed best and how was it evaluated?",{"text":86,"@type":78},"The SVM model showed superior predictive performance, assessed with ROC curves and calibration curves, and compared on metrics such as specificity, sensitivity, F1 score, and recall.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]