A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras
Our study emphasises the significance of analysing post-kidney transplant outcomes and identifying era-specific factors mitigating graft failure. The proposed framework can serve as a foundation for future research and assist physicians in identifying patients at risk of graft failure.
Authors
- June Fabian (ORCID: https://orcid.org/0000-0001-7130-9142)
- Blessing Ogbuokiri (ORCID: https://orcid.org/0000-0003-1606-0019)
- George Obaido (ORCID: https://orcid.org/0000-0001-5049-9522)
- Okechinyere Achilonu (ORCID: https://orcid.org/0000-0003-2911-0011)
- Eustasius Musenge (ORCID: https://orcid.org/0000-0002-3382-2372)
- Kehinde Aruleba (ORCID: https://orcid.org/0000-0003-3422-7046)
Institutions
- University of Leicester (GB)
- Brock University (CA)
- University of the Witwatersrand (ZA)
- Wits University Donald Gordon Medical Centre (ZA)
- University of California, Berkeley (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2024-09-03
- DOI
- https://doi.org/10.3389/fdgth.2024.1427845
- Citations
- 3
- Primary Topic
- Renal Transplantation Outcomes and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Wellcome Trust