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

Authors:
Chao-Cheng Lin;Ying-Chieh Wang;Jen-Yeu Chen;Ying-Jay Liou;Ya-Mei Bai;I-Ching Lai;Tzu-Ting Chen;Hung-Wen Chiu;Yu-Chuan Li
Affiliations:
Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taiwan and Department of Psychiatry, National Taiwan University Hospital and National Taiwan University Coll ...;Department of Psychiatry, Yu-Li Veterans Hospital, Taiwan;Department of Psychiatry, Yu-Li Veterans Hospital, Taiwan;Department of Psychiatry, Taipei Veterans General Hospital, Taiwan;Department of Psychiatry, Taipei Veterans General Hospital, Taiwan and Department of Psychiatry, College of Medicine, National Yang-Ming University, Taipei, Taiwan;Department of Psychiatry, Yu-Li Veterans Hospital, Taiwan;Department of Psychiatry, Yu-Li Veterans Hospital, Taiwan;Graduate Institute of Medical Informatics, Taipei Medical University, Taiwan;Institute of Biomedical Informatics, National Yang-Ming University, Taiwan
Venue:
Computer Methods and Programs in Biomedicine
Year:
2008

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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