Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients
The study concluded that ML models, notably the XGB algorithm, can effectively predict DSL in real-time using comprehensive clinical data, enhancing the clinical decision-making process for GC patients.
Authors
- Wookyoung Jung
- Sun-Hwi Hwang (ORCID: https://orcid.org/0000-0001-8058-0524)
- Yushin Kim (ORCID: https://orcid.org/0000-0002-2003-5740)
- Jae Hun Chung (ORCID: https://orcid.org/0000-0002-7821-5445)
- Dongjun Lee (ORCID: https://orcid.org/0000-0001-6828-401X)
- Dongwon Lim
- Si-Hak Lee
Institutions
- Pusan National University Yangsan Hospital (KR)
- Artificial Intelligence in Medicine (Canada) (CA)
- Hanyang University (KR)
- Pusan National University (KR)
Publication Details
- Journal
- Frontiers in Surgery
- Published
- 2025-05-06
- DOI
- https://doi.org/10.3389/fsurg.2025.1550990
- Citations
- 1
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 1.05
Funders
- Pusan National University
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion