Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.

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ID: 21854
2017
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Abstract
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the "derivation cohort" to develop dose-prediction algorithm, while the remaining 20% constituted the "validation cohort" to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.
Reference Key
tang2017applicationscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tang, Jie;Liu, Rong;Zhang, Yue-Li;Liu, Mou-Ze;Hu, Yong-Fang;Shao, Ming-Jie;Zhu, Li-Jun;Xin, Hua-Wen;Feng, Gui-Wen;Shang, Wen-Jun;Meng, Xiang-Guang;Zhang, Li-Rong;Ming, Ying-Zi;Zhang, Wei;
Journal Scientific reports
Year 2017
DOI
10.1038/srep42192
URL
Keywords Keywords not found

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