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PD31-09 DEVELOPMENT OF EXTREME GRADIENT BOOST (XGBOOST) MACHINE LEARNING MODEL USING AN INSTITUTIONAL PEDIATRIC KIDNEY TRANSPLANT DATABASE FOR PREDICTION OF DELAYED GRAFT FUNCTION
88 Citations•2023•
Jin Kyu (Justin) Kim, P. Yadav, M. Chua
The Journal of Urology
This novel model is the first attempt at predicting DGF in children undergoing kidney transplantation and holds promise for further development and improvement with additional variables and patient numbers.
Abstract
generated model had very high negative predictive value, allowing us to identify patients with high risk of DGF, providing an opportunity for closer monitoring. This novel model is the fi rst attempt at predicting DGF in children undergoing kidney transplantation and holds promise for further development and improvement with additional variables and patient numbers.