Current Result Document :
| 한글제목(Korean Title) |
긴급대응 시스템을 위한 심층 해석 가능 학습 |
| 영문제목(English Title) |
Deep Interpretable Learning for a Rapid Response System |
| 저자(Author) |
우엔 쫑 니아
보탄헝
고보건
이귀상
양형정
김수형
Trong-Nghia Nguyen
Thanh-Hung Vo
Bo-Gun Kho
Guee-Sang Lee
Hyung-Jeong Yang
Soo-Hyung Kim
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| 원문수록처(Citation) |
VOL 28 NO. 02 PP. 0805 ~ 0807 (2021. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.
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| 키워드(Keyword) |
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| 파일첨부 |
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