Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning
In this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.
Authors
- Wisit Cheungpasitporn (ORCID: https://orcid.org/0000-0001-9954-9711)
- Supawit Tangpanithandee (ORCID: https://orcid.org/0000-0001-6103-2338)
- Napat Leeaphorn (ORCID: https://orcid.org/0000-0003-3133-4479)
- Caroline C. Jadlowiec (ORCID: https://orcid.org/0000-0001-7860-9519)
- Matthew Cooper (ORCID: https://orcid.org/0000-0002-3438-9638)
- Supawadee Suppadungsuk (ORCID: https://orcid.org/0000-0003-1597-2411)
- Charat Thongprayoon (ORCID: https://orcid.org/0000-0002-8313-3604)
- Raymond L. Heilman (ORCID: https://orcid.org/0000-0003-0367-2008)
Institutions
- Mayo Clinic (US)
- Medical College of Wisconsin (US)
- Mahidol University (TH)
- Jacksonville College (US)
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Clinical Transplantation
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1111/ctr.15470
- Citations
- 2
- Primary Topic
- Renal Transplantation Outcomes and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.02