[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127715-en":3,"doc-seo-127715-105":31,"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":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},127715,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Prediction of Intraoperative Red Blood Cell Transfusion in Valve Replacement Surgery - Machine Learning Algorithm Development Based on Non-anemic Cohort","Develop machine learning algorithms to predict intraoperative red blood cell transfusion during valve replacement surgery using a preoperative dataset restricted to a non-anemic cohort. Enroll 423 patients from January 2015 to December 2020 and integrate demographics, clinical status, and preoperative biochemistry results to train multiple models including decision tree, random forest, XGBoost, CatBoost, support vector classifier, and logistic regression. Evaluate performance using AUC, accuracy, recall, precision, and F1 score, and interpret the best model with SHAP.","TYPE Original Research PUBLISHED 29 February 2024 DOI 10.3389/fcvm.2024.1344170  \nEDITED BY  \nYongfeng Shao,  \nNanjing Medical University, China  \nREVIEWED BY  \nAntonino S. Rubino,  \nUniversity of Campania Luigi Vanvitelli, Italy Wei He,  \nSoutheast University, China  \n*CORRESPONDENCE  \nMin Yu  \n [minyudr@163.com](minyudr@163.com)[ ](minyudr@163.com)Haiqing Li  \n [drlihaiqing@163.com](drlihaiqing@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 25 November 2023  \nACCEPTED 20 February 2024  \nPUBLISHED 29 February 2024  \nCITATION  \nZhou R, Li Z, Liu J, Qian D, Meng X, Guan L, Sun X, Li H and Yu M (2024) Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.  \nFront. Cardiovasc. Med. 11:1344170 .  \ndoi: 10.3389/fcvm.2024.1344170  \nCOPYRIGHT  \n© 2024 Zhou, Li, Liu, Qian, Meng, Guan, Sun, Li and Yu. 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.  \nPrediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort  \nRen Zhou1†, Zhaolong Li2†, Jian Liu3†, Dewei Qian3, Xiangdong Meng3, Lichun Guan3, Xinxin Sun4, Haiqing Li2* and Min Yu3*  \n1State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai Institute of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2Department of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 4Department of Cardiovascular Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China  \nBackground: Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort.  \nMethods: A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR) . Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model.  \nResults: The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662–0.780), which also got relatively high F1 score (0 . 695) . The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0 .534–0. 697) . The SHAP summary plot and the SHAP dependence plot were used to visually i","cbCain9YPgFsu7sU","https://ap.wps.com/l/cbCain9YPgFsu7sU","pdf",1518880,3,1,10,"English","en",105,"# Introduction\n# Methods\n## Model development\n## Feature selection and evaluation\n# Results\n## Predictive performance\n## Model interpretation with SHAP\n# Conclusions","[{\"question\":\"What is the study’s main goal?\",\"answer\":\"To develop machine learning algorithms that predict intraoperative red blood cell transfusion during valve replacement surgery using a preoperative non-anemic dataset.\"},{\"question\":\"Which patient data and time period were used to train the models?\",\"answer\":\"A total of 423 patients undergoing valvular replacement surgery from January 2015 to December 2020 were included, using demographic, clinical, and preoperative biochemistry test results.\"},{\"question\":\"Which model performed best and how was it interpreted?\",\"answer\":\"The CatBoost algorithm achieved the highest AUC and a relatively high F1 score. SHAP summary and dependence plots were used to visualize positive or negative effects of selected features.\"}]","Prediction of Intraoperative Red Blood Cell Transfusion in Valve Replacement Surgery - Machine Learning Algorithm Development Based on Non-anemic Cohort | PDF",1785941148,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"prediction-of-intraoperative-red-blood-cell-transfusion-in-valve-replacement-surgery-machine-learning-algorithm-development-based-on-non-anemic-cohort","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/prediction-of-intraoperative-red-blood-cell-transfusion-in-valve-replacement-surgery-machine-learning-algorithm-development-based-on-non-anemic-cohort/127715/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"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},"What is the study’s main goal?","Question",{"text":76,"@type":77},"To develop machine learning algorithms that predict intraoperative red blood cell transfusion during valve replacement surgery using a preoperative non-anemic dataset.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which patient data and time period were used to train the models?",{"text":81,"@type":77},"A total of 423 patients undergoing valvular replacement surgery from January 2015 to December 2020 were included, using demographic, clinical, and preoperative biochemistry test results.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and how was it interpreted?",{"text":85,"@type":77},"The CatBoost algorithm achieved the highest AUC and a relatively high F1 score. 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