[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122768-en":3,"doc-seo-122768-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122768,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Machine Learning for Detecting Blood Transfusion Needs Using Biosignals","Adequate oxygen delivery via red blood cells is essential for survival, yet identifying when patients need transfusion can be slow and challenging, particularly in cases like internal bleeding. This study leverages biosignals including ECG, PPG, blood pressure, oxygen saturation (SpO2), and respiration to detect transfusion necessity. Fourteen signal-derived features are used within an ensemble model combining extreme gradient boosting and random forest. A stratified five-fold cross-validation evaluates detection accuracy and ROC-AUC performance at 92.7% and 0.977.","Computer Systems Science & Engineering DOI: 10.32604/csse.2023.035641  \nArticle  \nMachine Learning for Detecting Blood Transfusion Needs Using Biosignals Hoon Ko1 , Chul Park2 , Wu Seong Kang3 , Yunyoung Nam4 , Dukyong Yoon5 and Jinseok Lee1 , *  \n1 Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University,  \nYongin, 17104, Korea  \n2 Department of Internal Medicine, Wonkwang University School of Medicine, Iksan, 54538, Korea  \n3 Department of Trauma Surgery, Jeju Regional Trauma Center, Cheju Halla General Hospital,  \nJeju, 63127, Korea  \n4 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea  \n5 Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, 03722, Korea  \n*Corresponding Author: Jinseok Lee. Email: [gonasago@khu.ac.kr](gonasago@khu.ac.kr)  \nReceived: 29 August 2022; Accepted: 08 December 2022  \nAbstract: Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately.  \nFor the model, this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest. The model was evaluated by a stratified five-fold crossvalidation: the detection accuracy and area under the receiver operating  \ncharacteristics were 92.7% and 0.977, respectively.  \nKeywords: Blood transfusion; ECG; PPG; pulse transit time; blood pressure;  \nmachine learning  \n1 Introduction  \nBlood volume, hematological values, and immune systems vary depending on various clinical situations, which change the response to hypovolemia or hypoxia situations [1] . Thus, blood transfusion must be carefully determined between its benefits and risks. For example, in adults, if the hemoglobin level decreases below 10 g/dL, an increase in cardiac output or redistribution in the organ may occur to improve oxygen transport capacity. In addition, it is known that the frequency and type of transfusion abnormalities that may occur during the transfusion process are different due to differences in underlying diseases [1,2] . According to the report from Korean Blood Safety Monitoring System in 2021 [3], a total of 2,847 blood transfusion-related symptoms were reported; 1,675 cases of febrile non-hemolytic transfusion reaction (FNHTR), 761 cases of an allergic reaction, 58 cases of  \nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n2370 CSSE, 2023, vol.46, no.2  \ntransfusion-associated dyspnea, 37 cases of transfusion-associated hypotensive reaction, 18 cases of transfusion-associated circulator overload, three cases of post-transfusion purpura, two cases of acute hemolytic transmission reaction, two cases of transfusion-related acute lung injury, and one case of a delayed serologic transfusion reaction. The FNHTR and allergic reaction accounted for 85 .6% of all blood transfusion-related symptoms.  \nEspecially for trauma patients, blood transfusion is one of the critical management [4] . Pretransfusion testing is essential for safe blood transfusion, but in situations such as severe trauma or massive bleeding, a delay in blood delivery in minutes has a specificity that causes sesecvere consequences for patients. In these emergency cases, even i","cbCaiuYIj4SpEyra","https://ap.wps.com/l/cbCaiuYIj4SpEyra","pdf",700627,1,13,"English","en",105,"# Introduction\n## Physiological rationale for transfusion decisions\n## Challenges in detecting transfusion need\n## Prior work and limitations\n## Motivation for machine learning with biosignals","[{\"question\":\"Why is detecting blood transfusion needs difficult in clinical practice?\",\"answer\":\"Detection can be time-consuming and sometimes not easily diagnosed, such as in internal bleeding.\"},{\"question\":\"Which biosignals and features are used for the proposed model?\",\"answer\":\"The study uses ECG, PPG, blood pressure, SpO2, and respiration, extracting 14 features from the physiological signals.\"},{\"question\":\"How is the model evaluated and what performance is reported?\",\"answer\":\"Evaluation uses stratified five-fold cross-validation, reporting detection accuracy of 92.7% and ROC-AUC of 0.977.\"}]","Machine Learning for Detecting Blood Transfusion Needs Using Biosignals | PDF",1785812799,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-detecting-blood-transfusion-needs-using-biosignals","",{"@graph":36,"@context":85},[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/machine-learning-for-detecting-blood-transfusion-needs-using-biosignals/122768/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is detecting blood transfusion needs difficult in clinical practice?","Question",{"text":75,"@type":76},"Detection can be time-consuming and sometimes not easily diagnosed, such as in internal bleeding.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which biosignals and features are used for the proposed model?",{"text":80,"@type":76},"The study uses ECG, PPG, blood pressure, SpO2, and respiration, extracting 14 features from the physiological signals.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model evaluated and what performance is reported?",{"text":84,"@type":76},"Evaluation uses stratified five-fold cross-validation, reporting detection accuracy of 92.7% and ROC-AUC of 0.977.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]