Graft survival in renal transplantation
| Article DOI | https://doi.org/10.1007/s00467-024-06484-5 |
| Objective | To identify factors affecting graft survival in pediatric kidney transplantation |
| AI Approach | Naïve Bayes, logistic regression, SVM, multi-layer perception, XGBoost |
| Data Source(s) | Single institutional series (465 patients) |
| Model Input | 48 variables from patient chart data, including patient demographic characteristics, number of HLA matches, transplant-related variables, and post-transplant complications. |
| Model Outcome | Graft failure |
| Model Metrics | F1 of 0.92, accuracy of 97% |
| Model Usability | Data available from the corresponding author. No publicly available code or tool. |
AI = Artificial intelligence, SVM = Support vector machines, AUC = Area under the receiver operator characteristic
AIPEDURO 
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