Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable models
Our models provide accurate, interpretable risk predictions and highlight key drivers of kidney nonuse, including variation across OPOs. These findings can inform the design of effective organ allocation interventions, increasing the likelihood of transplantation for hard-to-place kidneys.
Authors
- Osman Y. Özaltın (ORCID: https://orcid.org/0000-0002-0093-5645)
- Sait Tunç (ORCID: https://orcid.org/0000-0002-0194-7795)
- Matthew J. Ellis (ORCID: https://orcid.org/0000-0002-8467-8534)
- Ruoting Li (ORCID: https://orcid.org/0000-0002-9138-4606)
Institutions
- North Carolina State University (US)
- University of Pittsburgh (US)
- Duke University (US)
- Virginia Tech (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-08-13
- DOI
- https://doi.org/10.3389/frai.2025.1638574
- Citations
- 1
- Primary Topic
- Organ Donation and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 1.88