Machine learning for modeling and identifying risk factors of pancreatic fistula
This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction. These findings contribute to the advancement of personalized perioperative care and may guide targeted preventive strategies.
Authors
- N.A. Grishin (ORCID: https://orcid.org/0000-0003-1703-9115)
- Sergei Anatolyevich Ivanov
- Vladimir S. Trifanov (ORCID: https://orcid.org/0000-0003-1879-6978)
- П. В. Соколов (ORCID: https://orcid.org/0000-0002-5050-7868)
- А. Д. Каприн (ORCID: https://orcid.org/0000-0001-8784-8415)
- M. B. Potievskiy (ORCID: https://orcid.org/0000-0002-8514-8295)
- Л. О. Петров (ORCID: https://orcid.org/0000-0001-6272-9647)
- R.I. Moshurov (ORCID: https://orcid.org/0000-0002-5676-4224)
- Peter Shegai (ORCID: https://orcid.org/0000-0001-8901-4596)
Institutions
- Peoples' Friendship University of Russia (RU)
- Medical Radiological Research Center (RU)
- P.A. Hertzen Moscow Oncology Research Institute (RU)
Publication Details
- Journal
- World Journal of Gastrointestinal Oncology
- Published
- 2025-03-24
- DOI
- https://doi.org/10.4251/wjgo.v17.i4.100089
- Citations
- 2
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 2.09