Survival After Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model
The ML model predicting survival after radical cystectomy exhibited bias across sex and racial subgroups. By using algorithm unfairness mitigation techniques, we improved algorithmic fairness as measured by the eOR. Our study highlights the role of not only evaluating for model bias but also actively mitigating such disparities to ensure equitable health care delivery. We also deployed the first web-based fair ML model for predicting survival after radical cystectomy.
Authors
- Rajesh Ranganath (ORCID: https://orcid.org/0000-0003-2854-3376)
- Hyungrok Do (ORCID: https://orcid.org/0000-0001-5317-6809)
- Madhur Nayan (ORCID: https://orcid.org/0000-0001-7502-3475)
- Samuel Carbunaru (ORCID: https://orcid.org/0000-0001-5297-3611)
- Katie S. Murray (ORCID: https://orcid.org/0000-0002-2633-9438)
- Yassamin Neshatvar
Institutions
- New York City Health and Hospitals Corporation (US)
- Courant Institute of Mathematical Sciences (US)
- Bellevue Hospital Center (US)
- New York University (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-10-06
- DOI
- https://doi.org/10.2196/63289
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21