A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study
This deep-learning-based PO-AKI risk prediction model provides a comprehensive approach to evaluating PO-AKI risk prediction by combining preoperative clinical data with real-time intraoperative vital sign information, offering enhanced predictive performance for better clinical decision-making.
Authors
- Jeonghwan Lee (ORCID: https://orcid.org/0000-0003-3199-635X)
- Yisak Kim (ORCID: https://orcid.org/0000-0002-5420-1436)
- Jiwon Ryu (ORCID: https://orcid.org/0000-0001-7927-9654)
- Jeong Min Cho (ORCID: https://orcid.org/0000-0001-7643-994X)
- Soie Kwon (ORCID: https://orcid.org/0000-0002-0878-5469)
- Eunbyeol Cho
- Edward Choi (ORCID: https://orcid.org/0000-0002-5958-3509)
- Seung Min Chung (ORCID: https://orcid.org/0000-0003-3336-7557)
- Sejoong Kim (ORCID: https://orcid.org/0000-0002-7238-9962)
- Sehoon Park (ORCID: https://orcid.org/0000-0002-4221-2453)
- Sunah Yang (ORCID: https://orcid.org/0000-0001-6332-5396)
- Hyung‐Jin Yoon (ORCID: https://orcid.org/0000-0003-4432-4894)
- Hajeong Lee (ORCID: https://orcid.org/0000-0002-1873-1587)
- Kwangsoo Kim (ORCID: https://orcid.org/0000-0002-3011-2666)
- Min Jae Lee
Institutions
- Seoul National University (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Seoul National University Hospital (KR)
- Seoul National University Bundang Hospital (KR)
- SNUH SMG-SNU Boramae Medical Center (KR)
- Chung-Ang University Hospital (KR)
Publication Details
- Journal
- PLoS Medicine
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1371/journal.pmed.1004566
- Citations
- 5
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.94
Funders
- Ministry of Health and Welfare
- Ministry of Science and ICT, South Korea