Advanced Prediction of Glomerular Filtration Rate After Kidney Transplantation Using Gradient Boosting Techniques.
Our model underscored the promise of advanced machine learning techniques in refining prediction of glomerular filtration rate after kidney transplant. With its augmented precision, the model can support clinicians in making informed decisions regarding early biopsies and interventions, thus highlighting the vital role of sophisticated analytical methods in medical prognosis and the monitoring of pediatric patient care.
Authors
- Emre Karakaya (ORCID: https://orcid.org/0000-0002-4879-7974)
- Esra Baskın (ORCID: https://orcid.org/0000-0003-4361-8508)
- Kaan Gülleroğlu (ORCID: https://orcid.org/0000-0003-1434-3824)
- Meraj Alam Siddiqui (ORCID: https://orcid.org/0000-0002-5739-6590)
- Adem Şafak (ORCID: https://orcid.org/0000-0003-2817-1920)
- Mehmet Haberal (ORCID: https://orcid.org/0000-0002-3462-7632)
Institutions
- Başkent University (TR)
Publication Details
- Journal
- Experimental and Clinical Transplantation
- Published
- 2024-10-01
- DOI
- https://doi.org/10.6002/ect.pedsymp2024.o18
- Primary Topic
- Renal and Vascular Pathologies
- Type
- article