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Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 조, 재형 | - |
| dc.date.accessioned | 2022-11-16T06:45:33Z | - |
| dc.date.available | 2022-11-16T06:45:33Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/22724 | - |
| dc.format | application/pdf | - |
| dc.language.iso | en | - |
| dc.title | Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality | - |
| dc.title.alternative | 수술 직후의 수집된 혈액 검사 수치 기반의 수술 후 사망율 예측 모델 개발 및 검증: 기계학습접근법 | - |
| dc.type | Thesis | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000031820 | - |
| dc.subject.keyword | American Society of Anesthesiologists physical status | - |
| dc.subject.keyword | surgery | - |
| dc.subject.keyword | surgical Apgar score | - |
| dc.subject.keyword | Machine learning | - |
| dc.subject.keyword | Observational study | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | 대학원 의생명과학과 | - |
| dc.contributor.affiliatedAuthor | 조, 재형 | - |
| dc.date.awarded | 2022 | - |
| dc.type.local | Theses | - |
| dc.citation.date | 2022 | - |
| dc.embargo.liftdate | 9999-12-31 | - |
| dc.embargo.terms | 9999-12-31 | - |
- Appears in Collections:
- Theses > Graduate School of Biomedical Sciences > Doctor
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