Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data

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Please use this identifier to cite or link to this item: http://libir.tmu.edu.tw/handle/987654321/1728

Title:	Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data
Authors:	李友專;邱泓文 Lin CC;Wang YC;Chen JY;Bai YM;Lai IC;Chen TT;Chiu HW;Li YC
Contributors:	醫學資訊研究所
Keywords:	Neural network models;Clozapine;Genetic polymorphism;Schizophrenia
Date:	2008
Issue Date:	2009-08-17 11:16:05 (UTC+8)
Abstract:	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.
Relation:	computer methods and programs in biomedicine.(91):91-99.
Data Type:	article
Appears in Collections:	[ ] Periodical Articles

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