scholarly journals Erratum: Corrigendum: Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Jie Tang ◽  
Rong Liu ◽  
Yue-Li Zhang ◽  
Mou-Ze Liu ◽  
Yong-Fang Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Transplant ◽  
Renal Transplant Recipients ◽  
Transplant Recipients ◽  
Learning Models ◽  
Stable Dose ◽  
Machine Learning Models

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

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jie Tang ◽  
Rong Liu ◽  
Yue-Li Zhang ◽  
Mou-Ze Liu ◽  
Yong-Fang Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Transplant ◽  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Therapeutic Window ◽  
Support Vector ◽  
Renal Transplant Recipients ◽  
Learning Models ◽  
Stable Dose ◽  
Machine Learning Models

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.

scholarly journals Machine-learning models to predict tacrolimus dosage in liver transplant recipients

2020 ◽  
Vol 34 (1) ◽  
pp. S147-S147
Author(s):  
Jeong-Moo Lee ◽  
Soo Bin Yoon ◽  
Hyung-Chul Lee ◽  
Chul-Woo Jung ◽  
Suk Kyun Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplant ◽  
Transplant Recipients ◽  
Learning Models ◽  
Tacrolimus Dosage ◽  
Liver Transplant Recipients ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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