Machine Learning Risk Stratification for Older Breast Cancer Survivors: Clinical Care Implications
Machine learning models accurately predicted relevant adverse survivorship outcomes, driven primarily by non-cancer specific factors. Breast cancer survivors at high risk for adverse outcomes may benefit from more intensive care, whereas those at low risk may be more appropriately managed by primary care.
Authors
- Larissa Nekhlyudov (ORCID: https://orcid.org/0000-0001-6747-4209)
- Jason Rotter (ORCID: https://orcid.org/0000-0002-4386-332X)
- Sarah A. Birken (ORCID: https://orcid.org/0000-0002-0591-4800)
- Catherine M. Alfano (ORCID: https://orcid.org/0000-0001-6488-2186)
- Stephanie B. Wheeler (ORCID: https://orcid.org/0000-0002-3399-9047)
- Lisa P. Spees (ORCID: https://orcid.org/0000-0002-5680-9330)
- Caitlin B. Biddell (ORCID: https://orcid.org/0000-0002-1644-7187)
- Justin G. Trogdon (ORCID: https://orcid.org/0000-0001-5484-7870)
- Deborah K. Mayer (ORCID: https://orcid.org/0000-0001-7007-4978)
- Michaela A. Dinan (ORCID: https://orcid.org/0000-0003-4849-4743)
Institutions
- University of North Carolina at Chapel Hill (US)
- Brigham and Women's Hospital (US)
- Northwell Health (US)
- Harvard University (US)
- Yale Cancer Center (US)
- Yale University (US)
- Department of Health (ZA)
- UNC Lineberger Comprehensive Cancer Center
- Wake Forest University (US)
- Mathematica Policy Research (US)
Publication Details
- Journal
- Health Services Research
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1111/1475-6773.70005
- Primary Topic
- Cancer survivorship and care
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- American Cancer Society