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Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

DC Field Value Language
dc.contributor.advisor박, 래웅-
dc.contributor.author조, 재형-
dc.date.accessioned2022-11-16T06:45:33Z-
dc.date.available2022-11-16T06:45:33Z-
dc.date.issued2022-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/22724-
dc.formatapplication/pdf-
dc.language.isoen-
dc.titleMachine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality-
dc.title.alternative수술 직후의 수집된 혈액 검사 수치 기반의 수술 후 사망율 예측 모델 개발 및 검증: 기계학습접근법-
dc.typeThesis-
dc.identifier.urlhttp://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000031820-
dc.subject.keywordAmerican Society of Anesthesiologists physical status-
dc.subject.keywordsurgery-
dc.subject.keywordsurgical Apgar score-
dc.subject.keywordMachine learning-
dc.subject.keywordObservational study-
dc.description.degreeDoctor-
dc.contributor.department대학원 의생명과학과-
dc.contributor.affiliatedAuthor조, 재형-
dc.date.awarded2022-
dc.type.localTheses-
dc.citation.date2022-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
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