Machine Learning Accurately Predicts Patient-Reported Outcomes 1 Year After Breast Reconstruction
Machine learning algorithms can accurately predict PROs before breast reconstruction. Ultimately, this data-driven approach may streamline shared decision-making and enhance patient-centered care.
Authors
- Jonlin Chen (ORCID: https://orcid.org/0000-0001-6803-0933)
- Carrie Scharf Stern
- Ariel Gabay
- Ronnie Labib Shammas (ORCID: https://orcid.org/0000-0001-5468-8646)
- Chris Sidey‐Gibbons (ORCID: https://orcid.org/0000-0002-4732-7305)
- Lillian A. Boe (ORCID: https://orcid.org/0000-0001-6279-9987)
- Jonas A. Nelson (ORCID: https://orcid.org/0000-0003-0329-4422)
- Andrea L. Pusic (ORCID: https://orcid.org/0000-0003-4352-9409)
- Babak J. Mehrara (ORCID: https://orcid.org/0000-0001-5717-697X)
Institutions
- Brigham and Women's Hospital (US)
- Memorial Sloan Kettering Cancer Center (US)
- Oracle (United States) (US)
- Intel (United States) (US)
Publication Details
- Journal
- Annals of Surgery
- Published
- 2025-03-05
- DOI
- https://doi.org/10.1097/sla.0000000000006688
- Citations
- 2
- Primary Topic
- Breast Implant and Reconstruction
- Type
- article
- Field-Weighted Citation Impact
- 4.01