Prediction of delayed graft function after kidney transplantation using artificial intelligence and machine learning: A Systematic Review.

Neural network-based risk score for acute kidney injury after liver transplantation.

From data to decisions: Machine learning applications in kidney transplantation management

Kidney transplantation (KT) is a complex clinical process requiring accurate risk stratification, timely decision-making, and individualized management across all stages of care. Traditional clinical scores and center-specific algorithms often fail to capture the multidimensional interactions among donor, recipient...

Machine learning-based nomogram for acute kidney injury after liver transplantation

Abstract Acute kidney injury (AKI) after liver transplantation (LT) is a frequent complication associated with adverse outcomes, yet practical early postoperative prediction tools remain limited. This retrospective study developed and internally validated an interpretable model for AKI after LT using perioperative vari...

Machine learning prediction models for diabetic kidney disease: A systematic review and meta-analysis.

ML models show promising discriminative performance for DKD prediction, but the evidence is severely limited by high heterogeneity, lack of external validation and reporting of calibration metrics, and poor methodological transparency.

Leveraging Machine Learning Methods to Predict Post-Transplant Diabetes in Solid Organ Transplant Recipients

Post-transplant diabetes mellitus (PTDM) is a common complication following solid organ transplantation and is associated with increased morbidity and mortality. This study aimed to identify both generalizable and solid organ-specific predictors of PTDM with machine learning methods. In this study, we used...

Prediction models for infection after kidney transplantation: a systematic review

Background Infection after kidney transplantation is a leading cause of graft loss and mortality. Risk prediction models may facilitate early identification of high-risk recipients, yet their quality remains unclear. This study aimed to systematically evaluate the performance, risk of bias, and clinical applicability o...