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

Mental Health Surveillance and Epidemiology (95) JMIR Theme Issue: COVID-19 Special Issue (2554) Depression and Mood Disorders; Suicide Prevention (1691) Anxiety and Stress Disorders (1330) Outbreak and Pandemic Preparedness and Management (1826) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions 2023 (409) Infectious Diseases (non-STD/STI) (1726) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111) Machine Learning (2076) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1542)

Published on in Vol 8, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32876, first published .
Machine Learning–Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study

Machine Learning–Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study

Machine Learning–Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study

Authors of this article:

Katrina Hueniken1, 2 Author Orcid Image ;   Nibene Habib Somé2, 3, 4, 5, 6 Author Orcid Image ;   Mohamed Abdelhack1 Author Orcid Image ;   Graham Taylor7, 8 Author Orcid Image ;   Tara Elton Marshall2, 3, 4, 6, 9, 10 Author Orcid Image ;   Christine M Wickens2, 3, 4, 11, 12 Author Orcid Image ;   Hayley A Hamilton2, 3, 4 Author Orcid Image ;   Samantha Wells2, 3, 4, 6, 13, 14 Author Orcid Image ;   Daniel Felsky1, 13, 15, 16 Author Orcid Image
Article Authors Cited by (25) Tweetations (5) Metrics

Journals

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  11. Dolling-Boreham R, Mohan A, Abdelhack M, Elton-Marshall T, Hamilton H, Boak A, Felsky D. Identifying Psychosocial and Ecological Determinants of Enthusiasm In Youth: Integrative Cross-Sectional Analysis Using Machine Learning. JMIR Public Health and Surveillance 2024;10:e48705 View
  12. Lee S, Lee S, Lee J, Jo Y, Park E, Cha J. Fusion of multiple self-diagnostic questionnaires into optimal diagnostic cut-offs and factor analysis for depression characterization of the Korean university student group. BMC Psychiatry 2024;24(1) View
  13. Baird A, Xia Y. Applying analytics to sociodemographic disparities in mental health. Nature Mental Health 2025;3(1):124 View
  14. Bernal-Salcedoc J, Vélez Álvarez C, Tabares Tabares M, Murillo-Rendónd S, Gonzáles-Martínez G, Castaño-Ramírez O. Classification of depression in young people with artificial intelligence models integrating socio-demographic and clinical factors. Current Psychology 2025;44(9):7897 View
  15. Ng J, Lad M, Patel D, Wang A. Applications of machine learning in cannabis research: A scoping review. European Journal of Integrative Medicine 2025;74:102434 View
  16. Nikolova Y, Ruocco A, Felsky D, Lange S, Prevot T, Vieira E, Voineskos D, Wardell J, Blumberger D, Clifford K, Naik Dharavath R, Gerretsen P, Hassan A, Hope I, Irwin S, Jennings S, Le Foll B, Melamed O, Orson J, Pangarov P, Quigley L, Russell C, Shield K, Sloan M, Smoke A, Tang V, Valdes Cabrera D, Wang W, Wells S, Wickramatunga R, Sibille E, Quilty L. Cognitive Dysfunction in the Addictions (CDiA): protocol for a neuron-to-neighbourhood collaborative research program. Frontiers in Psychiatry 2025;16 View
  17. Tayarani-N. M, Shahid S. Detecting Anxiety via Machine Learning Algorithms: A Literature Review. IEEE Transactions on Emerging Topics in Computational Intelligence 2025;9(4):2634 View
  18. Chen J, Rao M, Wei Y, Zhou Q, Tao J, Wang S, Bi B. Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China. World Journal of Psychiatry 2025;15(8) View
  19. Koh Z, Serbetci D, Skues J, Murray G. Toward Digital Self-Monitoring of Mental Health in the General Population: Scoping Review of Existing Approaches to Self-Report Measurement. JMIR Mental Health 2025;12:e59351 View
  20. Arora P, Dahiya S. COVID-19-Induced Depression and Anxiety Detection: A Systematic Literature Review of Machine Learning and Deep Learning Techniques. New Generation Computing 2025;43(4) View

Books/Policy Documents

  1. Ismail D, Hastings P. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. View
  2. Patel S, Pundge A, Zebanaaz S, Akther N. Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022). View
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Conference Proceedings

  1. Sneha , Bhatia S, Batra M. 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). A Comparative Study of Machine Learning Algorithms in Predicting Mental Disorders View