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Open AccessArticle

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder

1
Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
2
Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea
3
Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841, Korea
4
Brain Convergence Research Center, Korea University Anam Hospital, Seoul 02841, Korea
5
Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul 02841, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2019, 10(11), 907; https://doi.org/10.3390/genes10110907
Received: 28 September 2019 / Revised: 25 October 2019 / Accepted: 29 October 2019 / Published: 7 November 2019
Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitation, an accurate antidepressant response prediction model is needed. Recently, several studies have proposed models that extract useful features such as neuroimaging biomarkers and genetic variants from patient data, and use them as predictors for predicting the antidepressant responses of patients. However, it is impossible to utilize all the different types of predictors when making a clinical decision on what drugs to prescribe for a patient. Although a machine learning-based antidepressant response prediction model has been proposed to overcome this problem, the model cannot find the most effective antidepressant for a patient. Based on a neural network, we propose an Antidepressant Response Prediction Network (ARPNet) model capturing high-dimensional patterns from useful features. Based on a literature survey and data-driven feature selection, we extract useful features from patient data, and use the features as predictors. In ARPNet, the patient representation layer captures patient features and the antidepressant prescription representation layer captures antidepressant features. Utilizing the patient and antidepressant prescription representation vectors, ARPNet predicts the degree of antidepressant response. The experimental evaluation results demonstrate that our proposed ARPNet model outperforms machine learning-based models in predicting antidepressant response. Moreover, we demonstrate the applicability of ARPNet in downstream applications in use case scenarios. View Full-Text
Keywords: major depressive disorder; antidepressant response prediction; patient representation; neural network major depressive disorder; antidepressant response prediction; patient representation; neural network
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Chang, B.; Choi, Y.; Jeon, M.; Lee, J.; Han, K.-M.; Kim, A.; Ham, B.-J.; Kang, J. ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder. Genes 2019, 10, 907.

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