ArticleActa psychiatrica Scandinavica2021
Combining machine learning algorithms for prediction of antidepressant treatment response.
Article in Acta psychiatrica Scandinavica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled 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 approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Beneficial effects of Silexan on co-occurring depressive symptoms in patients with subthreshold anxiety and anxiety disorders: randomized, placebo-controlled trials revisited.European archives of psychiatry and clinical neuroscience · 2023Pooled it
- Effects of home-use transcranial direct current stimulation on clusters of depressive symptoms: an ancillary analysis of the PSYLECT study.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Trial
- Pursuing personalized medicine for depression by targeting the lateral or medial prefrontal cortex with Deep TMS.JCI insight · 2023Trial
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Data driven versus standard practice policies for personalized antidepressant treatment: a retrospective cohort study of electronic health records.BMC psychiatry · 2026Article
- Neuro-semantic graph fusion for explainable depression risk trajectory mapping using a graph-enhanced RoBERTa framework.Frontiers in artificial intelligence · 2026Article
- Gene-environment-brain topology reveals clinical subtypes of depression in UK Biobank.Scientific reports · 2025Article
- Advancing paroxetine treatment in depression: predicting remission and plasma concentration, and validating and updating therapeutic reference ranges.Translational psychiatry · 2025Article
- Symptom networks in major depressive disorder and treatment response: special focus on TRD.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- 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
- Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.Physical and engineering sciences in medicine · 2024Article
- Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors.Frontiers in psychiatry · 2024Article
- A factor analytic comparison of three commonly used depression scales (HAMD, MADRS, BDI) in a large sample of depressed inpatients.BMC psychiatry · 2023Article
- A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder.Scientific reports · 2023Article
- Machine learning prediction model for treatment responders in patients with primary biliary cholangitis.JGH open : an open access journal of gastroenterology and hepatology · 2023Article
- AI-assisted prediction of differential response to antidepressant classes using electronic health records.NPJ digital medicine · 2023Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPredictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho-sociodemographic variables and their predictive impact on treatment outcome phenotypes. EXPERIMENTAL PROCEDURES: Treatment response was defined by a change of HAM-D 17-item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross-center validation design. In total, 88 predictors were implemented.
resultsClustering revealed four distinct HAM-D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability.
conclusionOur results support a decisive role for machine learning in the management of antidepressant treatment. Treatment- and symptom-specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.
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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.