A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques - Zhang, Qiwen; Tian, Xueke; Chen, Guang; Yu, Ze; Zhang, Xiaojian; Lu, Jingli; Zhang, Jinyuan; Wang, Peile; Hao, Xin; Huang, Yining; Wang, Zeyuan; Gao, Fei; et al. (Frontiers in Medicine, 2022)
Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcome. The aim of this study was to compare different machine learning and deep learning algorithms, and establish individualized dose prediction models by using the best performing algorithm. Therefore, among ten commonly used algorithms we compared, the TabNet algorithm outperformed than other algorithms with the highest R2 (0.813) and the lowest prediction error [mean absolute error (MAE) 0.464, mean square error (MSE) 0.615, and root mean square error (RMSE) 0.784]. In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides reference for the application of deep learning technique in tacrolimus dose estimation, and TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.