Optimizing Tacrolimus Dosing During Hospitalization After Kidney Transplantation: A Comparative Model Analysis

01 April 2025 : Original article

Sangkyun Mok

^{ABCDEF 1}, Sun Cheol Park

^{ABDEF 2*}, Sang Seob Yun^{AF 2}, Young Jun Park

^{AF 3}, Dongin Sin^{CD 4}, Jung K. Hyun

^{CD 5}

DOI: 10.12659/AOT.947768

Ann Transplant 2025; 30:e947768

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Table 8 Tacrolimus dose prediction performance metrics.

Model	Split	R2	MAE	MAPE	MSE	RMSE
XGBoost	0	0.857	0.727	0.2	1.12	1.058
	1	0.856	0.657	0.186	0.88	0.938
	2	0.905	0.543	0.124	0.515	0.718
	3	0.842	0.634	0.14	0.729	0.854
	4	0.802	0.729	0.148	1.528	1.236
Average (Split=5) (mean±SD)		0.852±0.037	0.658±0.077	0.160±0.032	0.954±0.389	0.961±0.198
Linear regression	0	0.869	0.706	0.186	1.028	1.014
	1	0.862	0.606	0.143	0.842	0.917
	2	0.922	0.472	0.107	0.425	0.652
	3	0.848	0.597	0.131	0.704	0.839
	4	0.8	0.726	0.152	1.54	1.241
Average (Split=5) (mean±SD)		0.860±0.044	0.621±0.102	0.144±0.029	0.908±0.416	0.933±0.218
Elastic net regression	0	0.869	0.702	0.186	1.025	1.012
	1	0.863	0.607	0.138	0.837	0.915
	2	0.923	0.473	0.107	0.416	0.645
	3	0.849	0.595	0.131	0.698	0.835
	4	0.8	0.731	0.151	1.539	1.241
Average (Split=5) (mean±SD)		0.861±0.044	0.622±0.102	0.143±0.029	0.903±0.419	0.930±0.220
R – coefficient of determination; MAE – mean absolute error; MAPE – mean absolute percentage error; MSE – mean squared error; RMSE – root mean square error.

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