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Machine Learning for Detecting Blood Transfusion Needs Using Biosignals
DC Field | Value | Language |
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dc.contributor.author | 윤덕용 | - |
dc.date.accessioned | 2023-04-20T08:28:43Z | - |
dc.date.available | 2023-04-20T08:28:43Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/194101 | - |
dc.description.abstract | 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. For 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 cross-validation: the detection accuracy and area under the receiver operating characteristics were 92.7% and 0.977, respectively. © 2023 CRL Publishing. All rights reserved. | - |
dc.description.statementOfResponsibility | open | - |
dc.relation.isPartOf | Computer Systems Science & Engineering | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Machine Learning for Detecting Blood Transfusion Needs Using Biosignals | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Hoon Ko | - |
dc.contributor.googleauthor | Chul Park | - |
dc.contributor.googleauthor | Wu Seong Kang | - |
dc.contributor.googleauthor | Yunyoung Nam | - |
dc.contributor.googleauthor | Dukyong Yoon | - |
dc.contributor.googleauthor | Jinseok Lee | - |
dc.identifier.doi | 10.32604/csse.2023.035641 | - |
dc.contributor.localId | A06062 | - |
dc.subject.keyword | Blood transfusion | - |
dc.subject.keyword | ECG | - |
dc.subject.keyword | PPG | - |
dc.subject.keyword | pulse transit time | - |
dc.subject.keyword | blood pressure | - |
dc.subject.keyword | machine learning | - |
dc.contributor.alternativeName | Yoon, Dukyong | - |
dc.contributor.affiliatedAuthor | 윤덕용 | - |
dc.citation.volume | 46 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 2369 | - |
dc.citation.endPage | 2381 | - |
dc.identifier.bibliographicCitation | Computer Systems Science & Engineering, Vol.46(2) : 2369-2381, 2023-01 | - |
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