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

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Tang J,Liu R,Zhang YL,Liu MZ,Hu YF,Shao MJ,Zhu LJ,Xin HW,Feng GW,Shang WJ,Meng XG,Zhang LR,Ming YZ,Zhang W

doi

10.1038/srep42192

subject

Has Abstract

pub_date

2017-02-08 00:00:00

pages

42192

issn

2045-2322

pii

srep42192

journal_volume

7

pub_type

杂志文章

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