Deep Learning–Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study
A well-developed AI-based model that provides high predictive power can contribute to the prevention of major in-hospital events by providing early predictive information to clinicians. Additionally, it plays a crucial role in effectively addressing unmet needs and challenges in terms of human resources and practical procedures.
Authors
- Kwang Joon Kim (ORCID: https://orcid.org/0000-0002-5554-8255)
- YOUNG KIM
- Ji‐Hyun Kim (ORCID: https://orcid.org/0000-0003-2701-4317)
- Ki-Byung Lee (ORCID: https://orcid.org/0000-0003-4535-776X)
- Yuhyun Choi
- Se Hee Cheon
Institutions
- Yonsei University (KR)
- Presbyterian Medical Center (KR)
- Korea Telecom (South Korea) (KR)
- Hallym University Chuncheon Sacred Heart Hospital (KR)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-06-24
- DOI
- https://doi.org/10.2196/72232
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35