ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder
Open accessAbstract
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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In a small testing dataset (N = 13) of patients with MDD, 80% of the patients who were predicted to respond to treatment did in fact respond to their prescribed antidepressants.
“In a small testing dataset (N = 13) of patients with MDD, 80% of the patients who were predicted to respond to treatment did in fact respond to their prescribed antidepressants.”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Chang et al., 2019
Section 3.2 describes testing predictions of response to prescribed antidepressants in patients with MDD and defines precision as the proportion of predicted responders who actually responded. Section 3.2.1 says the test set was 10% of 121 patients, but the passages in our copy do not establish an exact test-set size of 13. Section 3.2.3 reports improved precision without giving the claimed 80% figure, so those two numbers are not verified. (LLM, conf=0.76)
Experimental — accuracy not yet measuredA network was designed to output modeling about the predicted effectiveness of antidepressants for every patient, and outperformed baseline models both for prediction of response and of remission.
“A network was designed to output modeling about the predicted effectiveness of antidepressants for every patient, and outperformed baseline models both for prediction of response and of remission.”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Chang et al., 2019
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- Authors
- Chang, B., Choi, Y., Jeon, M., Lee, J., Han, K.-M., Kim, A., Ham, B.-J., & Kang, J.
- Affiliations
- Korea University (KR)
- Korea University Anam Hospital (KR)
- Corresponding author
- Ham, Byung-Joo
- Kang, Jaewoo
- Venue
- Genes
- Publisher
- MDPI AG
- Published
- 2019-11-07
- Volume
- 10
- Issue
- 11
- Pages
- 907
- ISSN
- 2073-4425 (ISSN-L)
- PMC
- PMC6895829
- OpenAlex
- W2985449889
- Cited by
- 35
- References
- 25
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 4 · 2023: 9 · 2022: 4 · 2021: 4 · 2020: 6
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Genes (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- antidepressant response prediction, major depressive disorder, neural network model, patient representation, treatment response prediction
- MeSH terms
- Antidepressive Agents, Depressive Disorder, Major, Major Depressive Disorder, Drug Resistance, Humans, Software, Neural Networks, Computer, Treatment Outcome
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