ArticleTranslational psychiatry2021
A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.Frontiers in psychiatry · 2024Pooled it
- Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders.Frontiers in psychiatry · 2026Article
- Care trajectories of people with mood disorders in Quebec using latent class and latent profile analysis methods.Journal of mood and anxiety disorders · 2025Article
- Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.JMIR nursing · 2024Article
- Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach.JMIR mental health · 2024Article
- Diagnosing psychiatric disorders from history of present illness using a large-scale linguistic model.Psychiatry and clinical neurosciences · 2023Article
- A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia.Healthcare (Basel, Switzerland) · 2023Article
- Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes.Frontiers in psychiatry · 2023Article
- The centrality of working memory networks in differentiating bipolar type I depression from unipolar depression: A task-fMRI study.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2023Article
- Dissecting clinical and biological heterogeneity in clinical states of bipolar disorder: a 10-year retrospective study from China.Frontiers in psychiatry · 2023Article
- Wearable chemical sensors for biomarker discovery in the omics era.Nature reviews. Chemistry · 2022Review
- Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries.International journal of environmental research and public health · 2022Article
- Using decision-analysis modelling to estimate the economic impact of the identification of unrecognised bipolar disorder in primary care: the untapped potential of screening.International journal of bipolar disorders · 2022Article
- The Current State and Validity of Digital Assessment Tools for Psychiatry: Systematic Review.JMIR mental health · 2022Review
- An approach for knowledge acquisition from a survey data by conducting Bayesian network modeling, adopting the robust coplot method.Journal of applied statistics · 2022Article
- The Effects of Research Activities on Biomedical Students' Mental Health: A National Cross-Sectional Study.Frontiers in psychiatry · 2022Article
- Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach.Biomedicines · 2021Article
- Toward an Extended Definition of Major Depressive Disorder Symptomatology: Digital Assessment and Cross-validation Study.JMIR formative research · 2021Article
- Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.Journal of personalized medicine · 2021Review
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 3 countries.
Funding
Abstract
The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.
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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.