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ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder

Authors
Chang, BuruChoi, YonghwaJeon, MinjiLee, JunhyunHan, Kyu-ManKim, AramHam, Byung-JooKang, Jaewoo
Issue Date
11월-2019
Publisher
MDPI
Keywords
major depressive disorder; antidepressant response prediction; patient representation; neural network
Citation
GENES, v.10, no.11
Indexed
SCIE
SCOPUS
Journal Title
GENES
Volume
10
Number
11
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/61976
DOI
10.3390/genes10110907
ISSN
2073-4425
Abstract
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.
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College of Medicine > Department of Medical Science > 1. Journal Articles
Graduate School > Department of Biomedical Sciences > 1. Journal Articles
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