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우엔쫑니아 ( Trong-nghia Nguyen ),보탄헝 ( Thanh-hung Vo ),고보건 ( Bo-gun Kho ),이귀상 ( Guee-sang Lee ),양형정 ( Hyung-jeong Yang ),김수형 ( Soo-hyung Kim ) 한국정보처리학회 2021 한국정보처리학회 학술대회논문집 Vol.28 No.2
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.