Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea
The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.
Authors
- T.Y. Sim
- Chi‐Heum Cho (ORCID: https://orcid.org/0000-0002-0437-4099)
- Kyung Hyun Lee (ORCID: https://orcid.org/0000-0002-7010-5284)
- Gyeong Im Yu (ORCID: https://orcid.org/0000-0003-1494-3977)
- Sangchul Hahn (ORCID: https://orcid.org/0000-0003-2428-9534)
- Eunkyeong Yun (ORCID: https://orcid.org/0000-0002-2274-0806)
- Sun Hyo Park (ORCID: https://orcid.org/0000-0002-9464-0652)
- Kwang Joon Kim (ORCID: https://orcid.org/0000-0002-5554-8255)
- Ye Eun Jeong (ORCID: https://orcid.org/0009-0007-8063-2233)
- Eun Yeong Ha (ORCID: https://orcid.org/0000-0002-9534-4329)
- Ki-Byung Lee (ORCID: https://orcid.org/0000-0001-9106-5528)
- Ho‐Chan Cho (ORCID: https://orcid.org/0000-0003-0712-7728)
- In‐Cheol Kim (ORCID: https://orcid.org/0000-0002-5751-2328)
- Byung Eun Ahn (ORCID: https://orcid.org/0000-0001-7126-7966)
- Ji‐Hyun Kim (ORCID: https://orcid.org/0000-0003-2701-4317)
- Eun Young Cho (ORCID: https://orcid.org/0000-0002-4778-1276)
Institutions
- Yonsei University (KR)
- Protein Metrics (United States) (US)
- Hallym University Chuncheon Sacred Heart Hospital (KR)
- Hallym University Medical Center (KR)
- Keimyung University (KR)
Publication Details
- Journal
- Acute and Critical Care
- Published
- 2025-05-30
- DOI
- https://doi.org/10.4266/acc.000525
- Citations
- 2
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.51