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

Mental Health Surveillance and Epidemiology (62) JMIR Theme Issue: COVID-19 Special Issue (2452) Depression and Mood Disorders; Suicide Prevention (1363) Anxiety and Stress Disorders (1053) Outbreak and Pandemic Preparedness and Management (1727) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (383) Infectious Diseases (non-STD/STI) (1575) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514) Machine Learning (1465) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

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 (14) Tweetations (5) Metrics

Journals

  1. Raheja S. Analysis of Psychological Distress During COVID-19 Among Professionals. International Journal of Software Innovation 2022;10(1):1 View
  2. Majcherek D, Kowalski A, Lewandowska M. Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries. International Journal of Environmental Research and Public Health 2022;19(19):11913 View
  3. Shen N, Kassam I, Chen S, Ma C, Wang W, Boparai N, Jankowicz D, Strudwick G. Canadian perspectives of digital mental health supports: Findings from a national survey conducted during the COVID-19 pandemic. DIGITAL HEALTH 2022;8:205520762211022 View
  4. Rastpour A, McGregor C. Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach. JMIR Mental Health 2022;9(8):e38428 View
  5. Costa W, Pinheiro P, dos Santos N, Cabral L. Aligning the Goals Hybrid Model for the Diagnosis of Mental Health Quality. Sustainability 2023;15(7):5938 View
  6. Zhou C, Wheelock Å, Zhang C, Ma J, Dong K, Pan J, Li Z, Liang W, Gao J, Xu L. The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries. Frontiers in Public Health 2023;11 View
  7. Tariq M, Ismail S, Xu C. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLOS ONE 2024;19(3):e0294289 View
  8. Chan J, Marzuki A, Vafa S, Thanaraju A, Yap J, Chan X, Harris H, Todi K, Schaefer A. A systematic review on the relationship between socioeconomic conditions and emotional disorder symptoms during Covid-19: unearthing the potential role of economic concerns and financial strain. BMC Psychology 2024;12(1) View
  9. Zhou C, Wheelock Å, Zhang C, Ma J, Li Z, Liang W, Gao J, Xu L. Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework. Population Health Metrics 2024;22(1) View
  10. Khalili H, Wimmer M. Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic. Life 2024;14(7):783 View
  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

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
  3. . Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities. View