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Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques

DC FieldValueLanguage
dc.contributor.author김덕원-
dc.date.accessioned2014-12-18T09:01:13Z-
dc.date.available2014-12-18T09:01:13Z-
dc.date.issued2013-
dc.identifier.issn0140-0118-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/87381-
dc.description.abstractThis study sought to determine a mortality prediction model that could be used for triage in the setting of acute hemorrhage from trauma. To achieve this aim, various machine learning techniques were applied using the rat model in acute hemorrhage. Thirty-six anesthetized rats were randomized into three groups according to the volume of controlled blood loss. Measurements included heart rate (HR), systolic and diastolic blood pressures (SBP and DBP), mean arterial pressure, pulse pressure, respiratory rate, temperature, blood lactate concentration (LC), peripheral perfusion (PP), shock index (SI, SI = HR/SBP), and a new hemorrhage-induced severity index (NI, NI = LC/PP). NI was suggested as one of the good candidates for mortality prediction variable in our previous study. We constructed mortality prediction models with logistic regression (LR), artificial neural networks (ANN), random forest (RF), and support vector machines (SVM) with variable selection. The SVM model showed better sensitivity (1.000) and area under curve (0.972) than the LR, ANN, and RF models for mortality prediction. The important variables selected by the SVM were NI and LC. The SVM model may be very helpful to first responders who need to make accurate triage decisions and rapidly treat hemorrhagic patients in cases of trauma.-
dc.description.statementOfResponsibilityopen-
dc.relation.isPartOfMEDICAL & BIOLOGICAL ENGINEERING & COMPUTING-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHAnimals-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHBody Temperature-
dc.subject.MESHHemodynamics/physiology-
dc.subject.MESHLactic Acid/blood-
dc.subject.MESHLogistic Models-
dc.subject.MESHMale-
dc.subject.MESHModels, Statistical*-
dc.subject.MESHROC Curve-
dc.subject.MESHRandom Allocation-
dc.subject.MESHRats-
dc.subject.MESHRats, Sprague-Dawley-
dc.subject.MESHRespiratory Rate/physiology-
dc.subject.MESHShock, Hemorrhagic/physiopathology*-
dc.subject.MESHStatistics, Nonparametric-
dc.subject.MESHSupport Vector Machine-
dc.titleMortality prediction of rats in acute hemorrhagic shock using machine learning techniques-
dc.title.alternative기계학습방법을 이용한 백서에서의 급성 출혈성쇼크의 사망예측-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학)-
dc.contributor.googleauthorKyung-Ah Kim-
dc.contributor.googleauthorJoon Yul Choi-
dc.contributor.googleauthorTae Keun Yoo-
dc.contributor.googleauthorSung Kean Kim-
dc.contributor.googleauthorKilSoo Chung-
dc.contributor.googleauthorDeok Won Kim-
dc.identifier.doi10.1007/s11517-013-1091-0-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA00376-
dc.relation.journalcodeJ02195-
dc.identifier.eissn1741-0444-
dc.identifier.pmid23793529-
dc.identifier.urlhttp://link.springer.com/article/10.1007%2Fs11517-013-1091-0-
dc.subject.keywordHemorrhagic shock-
dc.subject.keywordRat-
dc.subject.keywordMortality-
dc.subject.keywordMachine learning-
dc.subject.keywordSupport vector machine-
dc.contributor.alternativeNameKim, Deok Won-
dc.contributor.affiliatedAuthorKim, Deok Won-
dc.rights.accessRightsnot free-
dc.citation.volume51-
dc.citation.number9-
dc.citation.startPage1059-
dc.citation.endPage1067-
dc.identifier.bibliographicCitationMEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol.51(9) : 1059-1067, 2013-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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