Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach
ML can predict short-term postoperative outcomes in breast reconstruction patients. With further model refinement and data quality optimization, these models may improve preoperative risk stratification and patient outcomes in breast reconstruction.
Authors
- Jonlin Chen (ORCID: https://orcid.org/0000-0001-6803-0933)
- Carrie Scharf Stern
- Ariel Gabay
- Chris Sidey‐Gibbons (ORCID: https://orcid.org/0000-0002-4732-7305)
- Jonas A. Nelson (ORCID: https://orcid.org/0000-0003-0329-4422)
- Babak J. Mehrara (ORCID: https://orcid.org/0000-0001-5717-697X)
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Oracle (United States) (US)
Publication Details
- Journal
- Journal of Surgical Oncology
- Published
- 2025-06-19
- DOI
- https://doi.org/10.1002/jso.70015
- Citations
- 2
- Primary Topic
- Reconstructive Surgery and Microvascular Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.01