Evolutionary ensemble learning algorithm to modeling of warfarin dose prediction for Chinese
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Tao:2018:ieeeJBHI,
-
author = "Yanyun Tao and Yenming J. Chen and Xiangyu Fu and
Bin Jiang and Yuzhen Zhang",
-
journal = "IEEE Journal of Biomedical and Health Informatics",
-
title = "Evolutionary ensemble learning algorithm to modeling
of warfarin dose prediction for Chinese",
-
year = "2018",
-
keywords = "genetic algorithms, genetic programming, warfarin dose
prediction, ensemble modeling, machine learning,
regression model, genetic programming",
-
ISSN = "2168-2194",
-
DOI = "doi:10.1109/JBHI.2018.2812165",
-
size = "12 pages",
-
abstract = "An evolutionary ensemble modelling (EEM) method is
developed to improve the accuracy of warfarin dose
prediction. In EEM, genetic programming (GP) evolves
diverse base models, and genetic algorithm optimises
the parameters of the GP. The EEM model is assembled by
using the prepared based models through a technique
called bagging. In the experiment, a dataset of 289
Chinese patients, which is provided by The First
Affiliated Hospital of Soochow University, is used for
training, validation, and testing. The EEM model with
selected feature groups is benchmarked with four
machine-learning methods and three conventional
regression models. Results show that the EEM model with
M2+G group, namely, age, height, weight, gender,
CYP2C9, VKORC1, and amiodarone, presents the largest
coefficients of determination (R2), highest percentage
of predicted dose within 20percent of the actual dose
(20percent-p), smallest mean absolute error (mae), mean
squared error (mse), root-mse on the test set, and the
least decrease in R2 from the training set to the test
set. In conclusion, the EEM method with M2+G delivers
superior performance and can therefore be a suitable
prediction model of warfarin dose for clinical
application.",
-
notes = "Soochow University, Suzhou, China. National Kaohsiung
University of Science & Technology, Taiwan, R.O.C. The
First Affiliated Hospital of Soochow University,
Suzhou, China, Also known as \cite{8306883}",
- }
Genetic Programming entries for
Yanyun Tao
Yenming J Chen
Xiangyu Fu
Bin Jiang
Yuzhen Zhang
Citations