Phase-specific kidney graft failure prediction with machine learning model
Phase-specific ML models offer robust predictive performance for kidney graft failure, particularly in mid-term periods, supporting their integration into dynamic post-transplant surveillance strategies. These models can aid clinicians in identifying high-risk patients and tailoring follow-up protocols to optimize long-term transplant outcomes.
Authors
- Shuzo Kobayashi (ORCID: https://orcid.org/0000-0003-2373-814X)
- Ainur Yerkos
- Sumi Hidaka (ORCID: https://orcid.org/0000-0002-1576-0868)
- Amankeldi A. Salybekov (ORCID: https://orcid.org/0000-0001-5490-9365)
- Markus Wolfien (ORCID: https://orcid.org/0000-0002-1887-4772)
- Zholdas Buribayev (ORCID: https://orcid.org/0000-0002-3486-227X)
Institutions
- Al-Farabi Kazakh National University (KZ)
- German Research Centre for Artificial Intelligence (DE)
- Regenerative Medicine Institute (MX)
- Shonan Kamakura General Hospital (JP)
- Carl Gustav Carus-Institut (DE)
- University Hospital Carl Gustav Carus (DE)
- Center for Scalable Data Analytics and Artificial Intelligence (DE)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3389/frai.2025.1682639
- Primary Topic
- Renal Transplantation Outcomes and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science