ArticleGenes2019
ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Pharmaco-Multiomics: A New Frontier in Precision Psychiatry.International journal of molecular sciences · 2025Review
- Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment.World journal of psychiatry · 2024Review
- Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study.JMIR formative research · 2023Article
- AI-assisted prediction of differential response to antidepressant classes using electronic health records.NPJ digital medicine · 2023Article
- Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.Brain informatics · 2023Review
- Precision Medicine Approaches to Mental Health Care.Physiology (Bethesda, Md.) · 2023Review
- Article
- Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis.BMC medical imaging · 2022Article
- Precision medicine in mood disorders.PCN reports : psychiatry and clinical neurosciences · 2022Review
- Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2021Review
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Exploring the Role of Gut Microbiota in Major Depressive Disorder and in Treatment Resistance to Antidepressants.Biomedicines · 2020Article
- Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches.International journal of molecular sciences · 2020Review
- Challenges and Future Prospects of Precision Medicine in Psychiatry.Pharmacogenomics and personalized medicine · 2020Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.