ArticleMolecular psychiatry2016
Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.
Article in Molecular psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 4 of them syntheses that pooled it.
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Who cites it
92 citing papers in PubMed, 4 syntheses or guidelines pooled it, 263 citations in OpenAlex.
- Prediction models for longitudinal trajectories of depression and anxiety: a systematic review.Journal of affective disorders · 2026Pooled it
- Association between impaired glucose metabolism and long-term prognosis at the time of diagnosis of depression: Impaired glucose metabolism as a promising biomarker proposed through a machine-learning approach.European psychiatry : the journal of the Association of European Psychiatrists · 2023Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Psychotherapy or medication for depression? Using individual symptom meta-analyses to derive a Symptom-Oriented Therapy (SOrT) metric for a personalised psychiatry.BMC medicine · 2020Pooled it
- Somatic and sociodemographic predictors of depression outcome among depressed patients with coronary artery disease - a secondary analysis of the SPIRR-CAD study.BMC psychiatry · 2019Trial
- Toward Personalized Psychoeducational Interventions for Psychophysical Health: A Systematic Review and Meta-Analysis for Tailored Intervention Selection.Journal of personalized medicine · 2026Review
- Digital physiological biomarkers predict within-person symptom changes in complex chronic illness.NPJ digital medicine · 2026Article
- Using Machine Learning to Predict Depression among Adolescents Living with HIV in Uganda.Global social welfare : research, policy & practice · 2026Article
- Positive psychology 4.0: a science of consequence for a world that can no longer wait.Frontiers in psychology · 2026Article
- Key predictors of postpartum depression and anxiety symptoms among mothers in Kilifi, Kenya: a machine learning approach.Frontiers in psychiatry · 2026Article
- Personalizing Antidepressant Therapy: Integrating Pharmacogenomics, Therapeutic Drug Monitoring, and Digital Tools for Improved Depression Outcomes.Journal of personalized medicine · 2025Review
- Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis.Renal failure · 2025Article
- Optimizing treatment for depression in primary care using psychotherapy versus antidepressant medication in a low-resource setting: protocol for the OptimizeD randomized controlled trial.BMC psychiatry · 2025Article
- Prediction of suicidal attempts among Chinese adolescents with mood disorders: a clinical study using a machine learning approach.BMC psychiatry · 2025Article
- Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.Child psychiatry and human development · 2025Article
- Limitations of Correlation Coefficients in Research on Functional Connectomes and Psychological Processes.Human brain mapping · 2025Review
- Investigating psychotherapists' attitudes towards artificial intelligence in psychotherapy.BMC psychology · 2025Article
- Explainable Machine Learning in the Prediction of Depression.Diagnostics (Basel, Switzerland) · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Development of New Diagnostic Techniques: Machine Learning.Advances in experimental medicine and biology · 2025Review
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors at 6 institutions in 3 countries.
Funding
Abstract
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
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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.