ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2021
Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings.
Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Subtyping depression using brain-gastric electrophysiology for early prediction of antidepressant response: a multicentric clinical study.Frontiers in psychiatry · 2026Trial
- Utilizing depression symptom-based phenotypes to explore ketamine treatment response in major depression: The Bio-K multicenter trial.Journal of affective disorders · 2025Trial
- Current progress in targeted pharmacotherapy to treat symptoms of major depressive disorder: moving from broad-spectrum treatments to precision psychiatry.CNS spectrums · 2025Review
- Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.Bipolar disorders · 2025Article
- Review
- AI-assisted prediction of differential response to antidepressant classes using electronic health records.NPJ digital medicine · 2023Article
- Predicting treatment outcome in depression: an introduction into current concepts and challenges.European archives of psychiatry and clinical neuroscience · 2023Review
- Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.Journal of child psychology and psychiatry, and allied disciplines · 2022Article
- Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review.Journal of medical Internet research · 2022Article
- Effectiveness of common antidepressants: a post market release study.EClinicalMedicine · 2021Article
- Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.Translational psychiatry · 2021Article
- Machine Learning Prediction of Treatment Outcome in Late-Life Depression.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
17 authors at 8 institutions in 4 countries.
Funding
Abstract
Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.
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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.