Federated Learning for Predicting Major Postoperative Complications
We show federated learning to be a useful tool to train robust postoperative outcome prediction models from large-scale data across 2 hospitals.
Authors
- Jeremy A. Balch (ORCID: https://orcid.org/0000-0002-1826-7884)
- Parisa Rashidi (ORCID: https://orcid.org/0000-0003-4530-2048)
- Ziyuan Guan (ORCID: https://orcid.org/0009-0009-4824-6927)
- Benjamin Shickel (ORCID: https://orcid.org/0000-0002-5304-7027)
- Tezcan Ozrazgat-Baslanti
- Zhenhong Hu (ORCID: https://orcid.org/0000-0001-5105-5376)
- Tyler John Loftus (ORCID: https://orcid.org/0000-0001-5354-443X)
- Yingbo Ma (ORCID: https://orcid.org/0009-0001-5271-7025)
- Yuanfang Ren (ORCID: https://orcid.org/0000-0002-4716-2408)
- Yonggi Park
- Azra Bihorac
- Ayush Patel (ORCID: https://orcid.org/0009-0005-4999-2615)
Institutions
- University of Florida (US)
Publication Details
- Journal
- Annals of Surgery Open
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1097/as9.0000000000000573
- Citations
- 2
- Primary Topic
- Frailty in Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 2.45