[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126182-en":3,"doc-seo-126182-105":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},126182,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predictive survival modelings for HIV-related cryptococcosis - comparing machine learning approaches","HIV-associated cryptococcosis shows unstable clinical trajectories and persistently high mortality, leaving clinicians without sufficiently practical, tailored risk stratification tools. Using clinical and immunological data from 98 cases, the study applies unsupervised clustering, elastic net regularized Cox regression, and random survival forests to model disease severity and predict survival. Cytokine profiling reveals an immune phenotype with excessive inflammatory response linked to worse severity and survival. The elastic net model improves predictive performance (C-index, Brier score, and time-dependent AUC) and supports personalized clinical decision-making.","TYPE Original Research PUBLISHED 02 May 2025  \nDOI 10.3389/fcimb.2025.1542707  \nOPEN ACCESS  \nEDITED BY  \nZiad A. Memish,  \nAlfaisal University, Saudi Arabia  \nREVIEWED BY  \nMohamed Hamed, Mansoura University, Egypt Ying-Kui Jiang,  \nFudan University, China  \n*CORRESPONDENCE  \nBertram Müller-Myhsok  \n [bmm@psych.mpg.de](bmm@psych.mpg.de)  \n†These authors have contributed equally to this work  \nRECEIVED 10 December 2024  \nACCEPTED 07 April 2025  \nPUBLISHED 02 May 2025  \nCITATION  \nFu X, Wu L, Xun J, Pütz B, Zheng Z, Li Y, Shen Y, Lu H, Chen J and Müller-Myhsok B (2025) Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches.  \nFront. Cell. Infect. Microbiol. 15:1542707 .  \ndoi: 10.3389/fcimb.2025.1542707  \nCOPYRIGHT  \n© 2025 Fu, Wu, Xun, Pütz, Zheng, Li, Shen, Lu, Chen and Müller-Myhsok. This is an openaccess 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.  \nPredictive survival modelings for HIV-related cryptococcosis:  \ncomparing machine learning approaches  \nXuemin Fu 1†, Luling Wu 2,3†, Jingna Xun 2, Benno Pütz 1, Zhihang Zheng 4, Yanpeng Li 5,6, Yinzhong Shen 2, Hongzhou Lu 4, Jun Chen 2 and Bertram Müller-Myhsok 1*  \n1Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany, 2 Department of Infectious Diseases and Immunology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China, 3 Department of Endoscopy, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China, 4 National Clinical Research Center for Infectious Disease, Shenzhen Third Peoples Hospital, Second Hospital Affiliated to Southern University of Science and Technology,  \nShenzhen, China, 5Shanghai Public Health Clinical Center, Fudan University, Shanghai, China, 6 Hubei Jiangxia Laboratory, Wuhan, China  \nIntroduction: HIV-associated cryptococcosis is marked by unpredictable disease trajectories and persistently high mortality rates worldwide. Although improved risk stratification and tailored clinical management are urgently needed to enhance patient survival, such strategies remain limited.  \nMethods: We analyzed clinical and immunological data from 98 HIV-related cryptococcosis cases, employing machine learning techniques to model disease severity and predict survival outcomes. Our approach included unsupervised clustering, elastic net regularized Cox regression, and random survival forests. Model performance was rigorously assessed using the C-index, Brier score, Calibration and time-dependent AUC, with validation executed through a comprehensive, multi-replicated nested cross-validation framework.  \nResults: Through cytokine profiling, we identified an immune phenotype characterized by excessive inflammatory response (EXC), associated with greater disease severity, more frequent neurological symptoms, and poorer survival outcomes compared to the other two immune phenotypes, highlighting its potential significance in risk stratification. To further support clinical decision-making, we developed an elastic net regularized Cox regression model, achieving superior predictive accuracy with a mean C-index of 0.78 for 36-month outcomes and a mean Brier score of 0.13, outperforming both random survival forest and traditional Cox models. Time-dependent AUC analysis validated the model ’s robustness, with AUC values of 0 . 84 at 12 months and 0.79 at 36 months, indicating its reliability and potential clinical utility.  \nDiscussion: This study presents comprehensive and multidimensional approaches to overcome the challenges commonly encountered in real-world clinical settings. By applying cytokin","cbCaicAMg4hwA6U9","https://ap.wps.com/l/cbCaicAMg4hwA6U9","pdf",5781316,7,1,11,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What data and modeling methods are used to predict survival in HIV-related cryptococcosis?\",\"answer\":\"The study analyzes clinical and immunological data from 98 cases and applies unsupervised clustering, elastic net regularized Cox regression, and random survival forests.\"},{\"question\":\"What is the immune phenotype identified by cytokine profiling, and why is it important?\",\"answer\":\"Cytokine profiling identifies an immune phenotype characterized by excessive inflammatory response (EXC). It is associated with greater disease severity, more neurological symptoms, and poorer survival, indicating relevance for risk stratification.\"},{\"question\":\"How does the elastic net regularized Cox model perform compared with other approaches?\",\"answer\":\"It achieves higher predictive accuracy for 36-month outcomes, with reported improvements in mean C-index and Brier score, and time-dependent AUC values indicating robustness and potential clinical utility.\"}]","Predictive survival modelings for HIV-related cryptococcosis - comparing machine learning approaches | PDF",1785903652,28,{"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},"predictive-survival-modelings-for-hiv-related-cryptococcosis-comparing-machine-learning-approaches","",{"@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/predictive-survival-modelings-for-hiv-related-cryptococcosis-comparing-machine-learning-approaches/126182/",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","2026-08-23","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 data and modeling methods are used to predict survival in HIV-related cryptococcosis?","Question",{"text":77,"@type":78},"The study analyzes clinical and immunological data from 98 cases and applies unsupervised clustering, elastic net regularized Cox regression, and random survival forests.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the immune phenotype identified by cytokine profiling, and why is it important?",{"text":82,"@type":78},"Cytokine profiling identifies an immune phenotype characterized by excessive inflammatory response (EXC). It is associated with greater disease severity, more neurological symptoms, and poorer survival, indicating relevance for risk stratification.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the elastic net regularized Cox model perform compared with other approaches?",{"text":86,"@type":78},"It achieves higher predictive accuracy for 36-month outcomes, with reported improvements in mean C-index and Brier score, and time-dependent AUC values indicating robustness and potential clinical utility.","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"]