[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124293-en":3,"doc-seo-124293-105":30,"detail-sidebar-cat-0-en-105":95},{"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},124293,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Mortality predicting models for patients with infective endocarditis - a machine learning approach","Infective endocarditis (IE) is a fatal cardiovascular disease with heterogeneous clinical manifestations and rapid progression. Existing risk models can identify high-risk patients but show imperfect predictive performance and limited practical application. A single-centered retrospective study constructed mortality prediction models using four machine learning methods—LASSO logistic regression, random forest, support vector machine, and k-nearest neighbors—and evaluated them with 10-fold cross-validated AUC-ROC. Random forest achieved the best discrimination for in-hospital and 6-month mortality and highlighted key predictors such as bilirubin, NT-proBNP, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age.","Zi-yang et al. BMC Medical Informatics and Decision Making (2025) 25:229  \n[https://doi.org/10.1186/s12911-025-03025-4](https://doi.org/10.1186/s12911-025-03025-4)  \nBMC Medical Informatics and Decision Making  \nRESEARCH Open Access  \nMortality predicting models for patients with infective endocarditis: a machine learning approach  \nYang Zi-yang2†, Wang Qi4†, Xingyan Liu5, Haolin Li5, Shouhong Wang 1, Danqing Yu3,6* and Xuebiao Wei 1*  \nAbstract  \nBackground Infective endocarditis (IE) is a fatal cardiovascular disease with varied clinical manifestations but rapid progression. A series of existing risk models helped identify IE patients with high risk, but the imperfect predictive performance and limited application called for better predictive systems.  \nMethods The single-centered, retrospective observational study applied four machine learning methods for predictive model construction: LASSO logistic regression, random forest (RF), support vector machine (SVM), and  \nk-nearest neighbors (KNN) . A 10-fold cross-validated area under the receiver operating characteristic curve (AUC-ROC) was used for performance evaluation.  \nResults A total of 1705 patients with IE were enrolled in the study, with 119 in-hospital deaths and 178 deaths after 6-month follow-up. RF achieved the highest AUC-ROCs for in-hospital and six-month mortality prediction (in-hospital: 0. 83, 6-month: 0 . 85) . RF was also applied to assess variable importance. The following variables were selected by RF as top important predictors for both in-hospital and six-month mortality prediction: total bilirubin, N-terminal pro-B-type natriuretic peptide, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age.  \nConclusions A risk model with machine learning approach was integrated in purpose of prognosis prediction in IE patients, helping rapid risk stratification and in-time management clinically.  \nClinical trial number Not applicable.  \nHighlights  \nBy incorporating both surgical and non-surgical patients, this machine learning-based model enhances clinical applicability, enabling early risk stratification and personalized decision-making in IE management.  \nKeywords Model prediction, Infective endocarditis, Machine learning  \n†Yang Zi-yang and Wang Qi contributed equally to this work.  \n*Correspondence: Danqing Yu [gdydq100@126.com](gdydq100@126.com)[ ](gdydq100@126.com)Xuebiao Wei [weixuebiao@163.com](weixuebiao@163.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.  \nZi-yang et al. BMC Medical Informatics and Decision Making (2025) 25:229 Page 2 of 9  \nIntroduction  \nInfective endocarditis (IE) remains as a challenging cardiovascular disease with poor prognosis, though with generalization of antibiotic application and advanced surgical strategies [1]. With atypical clinical manifestationsand potential non-timely management, patients can easily devel","cbCaisWC8jrorRDi","https://ap.wps.com/l/cbCaisWC8jrorRDi","pdf",1794089,1,9,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What problem does the study address in infective endocarditis care?\",\"answer\":\"Existing IE risk models do not provide sufficiently accurate predictions and are limited in real-world use. The study aims to improve mortality prognostic systems for better early risk stratification and management.\"},{\"question\":\"Which machine learning methods were compared for building the mortality models?\",\"answer\":\"The study applied LASSO logistic regression, random forest, support vector machine, and k-nearest neighbors. Performance was assessed using 10-fold cross-validated AUC-ROC.\"},{\"question\":\"Which model performed best and for what endpoints?\",\"answer\":\"Random forest achieved the highest AUC-ROC for both in-hospital mortality prediction and 6-month mortality prediction. This indicates stronger discrimination across both time horizons.\"},{\"question\":\"What variables were identified as the most important predictors?\",\"answer\":\"Random forest selected total bilirubin, NT-proBNP, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age as top predictors for both in-hospital and 6-month mortality.\"}]","Mortality predicting models for patients with infective endocarditis - a machine learning approach | PDF",1785821414,23,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"mortality-predicting-models-for-patients-with-infective-endocarditis-a-machine-learning-approach","",{"@graph":36,"@context":89},[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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mortality-predicting-models-for-patients-with-infective-endocarditis-a-machine-learning-approach/124293/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in infective endocarditis care?","Question",{"text":75,"@type":76},"Existing IE risk models do not provide sufficiently accurate predictions and are limited in real-world use. The study aims to improve mortality prognostic systems for better early risk stratification and management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods were compared for building the mortality models?",{"text":80,"@type":76},"The study applied LASSO logistic regression, random forest, support vector machine, and k-nearest neighbors. Performance was assessed using 10-fold cross-validated AUC-ROC.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and for what endpoints?",{"text":84,"@type":76},"Random forest achieved the highest AUC-ROC for both in-hospital mortality prediction and 6-month mortality prediction. This indicates stronger discrimination across both time horizons.",{"name":86,"@type":73,"acceptedAnswer":87},"What variables were identified as the most important predictors?",{"text":88,"@type":76},"Random forest selected total bilirubin, NT-proBNP, albumin, diastolic blood pressure, fasting blood glucose, uric acid, and age as top predictors for both in-hospital and 6-month mortality.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,122,127,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},40,"healthcare",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]