Development and internal–external validation of a machine learning model to predict the risk for postoperative adverse events in patients with peptic ulcer perforation: a secondary cohort study of the perforated peptic ulcer analyzing project study
In this study, a machine learning model was developed and deployed to predict postoperative adverse events in patients with PPU. Further external validation is required for clinical use.
Authors
- T. Yokoyama
- Hiromasa Hoshi (ORCID: https://orcid.org/0009-0000-7402-8274)
- Koji Morishita
- Kei Ito (ORCID: https://orcid.org/0000-0002-3994-8478)
- Hikaru Odera
- Akira Endo (ORCID: https://orcid.org/0000-0001-6377-7296)
- Shigeru Yamagishi
- Akira Suekane
- Tomohiro Akutsu
- Hideto Shiraki
- Yasukazu Narita
- Taro Masuda
- Koji Ito
Institutions
- Fujisawa City Hospital (JP)
- Tsuchiura Kyodo General Hospital (JP)
- National Hospital Organization Mito Medical Center (JP)
- Matsudo City Hospital (JP)
- Ohta Nishinouchi Hospital (JP)
- National Disaster Medical Center (JP)
- Chiba Rosai Hospital (JP)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2026-02-18
- DOI
- https://doi.org/10.1097/js9.0000000000004942
- Primary Topic
- Helicobacter pylori-related gastroenterology studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00