Investigating surgeon performance metrics as key predictors of robotic herniorrhaphy outcomes using iterative machine learning models: retrospective study
This study showed the novel application of machine learning modelling to bridge objective performance indicators and clinical patient factors to postoperative clinical outcomes, demonstrating the relevance of dynamic intraoperative surgeon factors on clinical outcomes.
Authors
- Fahri Gökçal (ORCID: https://orcid.org/0000-0003-0832-7991)
- Abeselom Fanta (ORCID: https://orcid.org/0000-0003-0592-861X)
- Georges Kaoukabani (ORCID: https://orcid.org/0000-0002-1626-3126)
- Thomas H. Shin (ORCID: https://orcid.org/0000-0003-2076-270X)
- Omar Yusef Kudsi (ORCID: https://orcid.org/0000-0001-6723-0909)
- Mallory C. Shields (ORCID: https://orcid.org/0000-0002-3331-9910)
- Xi Liu (ORCID: https://orcid.org/0000-0002-5273-5645)
- Ali Tavakkoli
Institutions
- Tufts University (US)
- Brigham and Women's Hospital (US)
- Good Samaritan Medical Center (US)
- University of Virginia Medical Center (US)
- Kent Hastanesi (TR)
- Intuitive Surgical (United States) (US)
- Acıbadem Adana Hospital (TR)
Publication Details
- Journal
- BJS Open
- Published
- 2024-12-30
- DOI
- https://doi.org/10.1093/bjsopen/zrae160
- Citations
- 2
- Primary Topic
- Hernia repair and management
- Type
- article
- Field-Weighted Citation Impact
- 1.15