This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1698) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1000) Diagnostic Tools in Mental Health (327) Decision Support for Health Professionals (1548) Machine Learning (2083) Bipolar Disorders (74) Artificial Intelligence (2144) Tools, Programs and Algorithms (382)

Published on in Vol 11 (2024)

This is a member publication of University of Cambridge (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50738, first published .
Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach

Authors of this article:

Jiri Benacek1 Author Orcid Image ;   Nimotalai Lawal1 Author Orcid Image ;   Tommy Ong1 Author Orcid Image ;   Jakub Tomasik1 Author Orcid Image ;   Nayra A Martin-Key1 Author Orcid Image ;   Erin L Funnell1, 2 Author Orcid Image ;   Giles Barton-Owen2 Author Orcid Image ;   Tony Olmert1 Author Orcid Image ;   Dan Cowell2 Author Orcid Image ;   Sabine Bahn1, 2 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. de Azevedo Cardoso T, Kochhar S, Torous J, Morton E. Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions. JMIR Mental Health 2024;11:e58631 View
  2. Stolfi F, Abreu H, Sinella R, Nembrini S, Centonze S, Landra V, Brasso C, Cappellano G, Rocca P, Chiocchetti A. Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine. Frontiers in Psychiatry 2024;15 View
  3. Rohei M, Varathan K, Palaiahnakote S, Anuar N. Feature-based enhanced boosting algorithm for depression detection. PeerJ Computer Science 2025;11:e2981 View
  4. Tong J, Yuan J, Qin Y, Zhu N, Zhang T, Zhu X, Xu Y, Liu M, Zhang J, Sun X. Clinicians’ experience on the effectiveness of pharmacotherapy in patients with first-episode depression: a randomized controlled trial based on pharmacogenomics. Frontiers in Pharmacology 2025;16 View
  5. Funnell E, Martin-Key N, Tomasik J, Bahn S, Teles A. Identifying predictors of formal help-seeking for premenstrual symptoms: A machine learning analysis of symptom, functional impairment and barriers data. PLOS Mental Health 2025;2(8):e0000274 View
  6. Zhang H, Liao Y, Lin Z, Wen H, Pang T, Zhao X, Zhang W, Lou X, Chen C, Hu S, Liu Z, Xu X. Comorbidity of undiagnosed mood symptoms with dementia risk in multi-regional multi-ethnic adults: evidence from epidemiological findings and plasma metabolites. Epidemiology and Psychiatric Sciences 2025;34 View

Conference Proceedings

  1. Li N, Wang G. Proceedings of the 5th International Conference on Computer Information and Big Data Applications. Research on depression diagnosis based on the Machine Learning View
  2. Pratiwi M, Sanjaya S. Proceedings of the 2024 7th International Conference on Digital Medicine and Image Processing. Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data View