ARPnet: Antidepressant response prediction network for major depressive disorder

Buru Chang, Yonghwa Choi, Minji Jeon, Junhyun Lee, Kyu Man Han, Aram Kim, Byung Joo Ham, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Treating patients with major depressive disorder is challenging because it takes 6-8 weeks 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. Our source code that implements ARPNet is available at

Original languageEnglish
Article number907
Issue number11
Publication statusPublished - 2019 Nov

Bibliographical note

Funding Information:
Funding: This research was supported by the National Research Foundation of Korea (NRF-2017R1A2A1A17069645, NRF-2016M3A9A7916996 and NRF-2014M3C9A3063541).

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.


  • Antidepressant response prediction
  • Major depressive disorder
  • Neural network
  • Patient representation

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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