https://scholars.lib.ntu.edu.tw/handle/123456789/199095
標題: | Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data. | 作者: | Lin, CC Wang, YC Chen, JY Liou, YJ Bai, YM Lai, IC Chen, TT Chiu, HW Li, YC. |
關鍵字: | Clozapine; Genetic polymorphism; Neural network models; Schizophrenia | 公開日期: | 2008 | 起(迄)頁: | 91-99 | 來源出版物: | Computer Methods and Programs in Biomedicine | 摘要: | Although one third to one half of refractory schizophrenic patients responds to clozapine, however, there are few evidences currently that could predict clozapine response before the use of the medication. The present study aimed to train and validate artificial neural networks (ANN), using clinical and pharmacogenetic data, to predict clozapine response in schizophrenic patients. Five pharmacogenetic variables and five clinical variables were collated from 93 schizophrenic patients taking clozapine, including 26 responders. ANN analysis was carried out by training the network with data from 75% of cases and subsequently testing with data from 25% of unseen cases to determine the optimal ANN architecture. Then the leave-one-out method was used to examine the generalization of the models. The optimal ANN architecture was found to be a standard feed-forward, fully-connected, back-propagation multilayer perceptron. The overall accuracy rate of ANN was 83.3%, which is higher than that of logistic regression (LR) (70.8%). By using the area under the receiver operating characteristics curve as a measure of performance, the ANN outperformed the LR (0.821 ± 0.054 versus 0.579 ± 0.068; p < 0.001). The ANN with only genetic variables outperformed the ANN with only clinical variables (0.805 ± 0.056 versus 0.647 ± 0.066; p = 0.046). The gene polymorphisms should play an important role in the prediction. Further validation of ANN analysis is likely to provide decision support for predicting individual response. ? 2008. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/225762 | DOI: | 10.1016/j.cmpb.2008.02.004 | SDG/關鍵字: | Drug products; Genetic engineering; Mathematical models; Medical problems; Polymorphism; Clozapine; Genetic polymorphism; Neural network models; Schizophrenia; Neural networks; clozapine; accuracy; adult; area under the curve; article; artificial neural network; DNA polymorphism; drug response; female; genetic polymorphism; genetic variability; human; linear regression analysis; major clinical study; male; multivariate logistic regression analysis; perceptron; pharmacogenetics; prediction; receiver operating characteristic; schizophrenia; statistical model; treatment response; validation study; Adult; Aged; Antipsychotic Agents; Clozapine; Computer Simulation; Decision Support Systems, Clinical; Dose-Response Relationship, Drug; Drug Therapy, Computer-Assisted; Female; Humans; Male; Middle Aged; Models, Biological; Neural Networks (Computer); Outcome Assessment (Health Care); Pattern Recognition, Automated; Pharmacogenetics; Schizophrenia; Treatment Outcome |
顯示於: | 醫學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。