ArticlePharmaceuticals (Basel, Switzerland)2020
Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework.
Article in Pharmaceuticals (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Predictors of depression outcomes among university students following brief smartphone-based interventions.Npj mental health research · 2026Article
- Neuro-semantic graph fusion for explainable depression risk trajectory mapping using a graph-enhanced RoBERTa framework.Frontiers in artificial intelligence · 2026Article
- Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients.Schizophrenia (Heidelberg, Germany) · 2025Article
- Article
- Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA.IEEE journal of translational engineering in health and medicine · 2025Article
- Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.Depression and anxiety · 2025Article
- The role of gut microbiota and metabolomic pathways in modulating the efficacy of SSRIs for major depressive disorder.Translational psychiatry · 2024Article
- Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank.GeroScience · 2024Article
- Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract.Microorganisms · 2024Review
- AI-assisted prediction of differential response to antidepressant classes using electronic health records.NPJ digital medicine · 2023Article
- Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naïve Major Depressive Patients.Journal of personalized medicine · 2022Article
- 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
- Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.International journal of molecular sciences · 2021Review
- Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach.Journal of personalized medicine · 2021Article
- Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.Scientific reports · 2021Article
- Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.Scientific reports · 2021Article
- Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments.
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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.