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

JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (383) JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1465) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Users' and Patients' Needs for Mental Health Services (403) Anxiety and Stress Disorders (1053) Infectious Diseases (non-STD/STI) (1575) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 8, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25097, first published .
Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study

Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study

Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study

Authors of this article:

Indra Prakash Jha1 Author Orcid Image ;   Raghav Awasthi1 Author Orcid Image ;   Ajit Kumar2 Author Orcid Image ;   Vibhor Kumar1 Author Orcid Image ;   Tavpritesh Sethi1 Author Orcid Image
Article Authors Cited by (28) Tweetations (6) Metrics

Journals

  1. Yiğitoğlu G, Keskin G, Köktaş N. The traumatic impact of the COVID-19 pandemic: the possible role of rumination and uncertainty. Current Psychology 2024;43(13):12311 View
  2. Singh S, Bedi D. Financial Disruption and Psychological Underpinning During COVID-19: A Review and Research Agenda. Frontiers in Psychology 2022;13 View
  3. Peng X, Wang-Trexler N, Magagna W, Land S, Peck K. Learning Agility of Learning and Development Professionals in the Life Sciences Field During the COVID-19 Pandemic: Empirical Study. Interactive Journal of Medical Research 2022;11(1):e33360 View
  4. Alle S, Kanakan A, Siddiqui S, Garg A, Karthikeyan A, Mehta P, Mishra N, Chattopadhyay P, Devi P, Waghdhare S, Tyagi A, Tarai B, Hazarik P, Das P, Budhiraja S, Nangia V, Dewan A, Sethuraman R, Subramanian C, Srivastava M, Chakravarthi A, Jacob J, Namagiri M, Konala V, Dash D, Sethi T, Jha S, Agrawal A, Pandey R, Vinod P, Priyakumar U, Nguyen B. COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits. PLOS ONE 2022;17(3):e0264785 View
  5. Raheja S. Analysis of Psychological Distress During COVID-19 Among Professionals. International Journal of Software Innovation 2022;10(1):1 View
  6. Ji L, Liu Z, Qu Z. [Retracted] Analysis of the Effects of Arts and Crafts in Public Mental Health Education Based on Artificial Intelligence Technology. Journal of Environmental and Public Health 2022;2022(1) View
  7. Gupta P, Maji S, Mehra R. Predictive Modeling of Stress in the Healthcare Industry During COVID-19. International Journal of Decision Support System Technology 2022;15(1):1 View
  8. Iantovics L, Enăchescu C. Method for Data Quality Assessment of Synthetic Industrial Data. Sensors 2022;22(4):1608 View
  9. Liu Y, Yin Y. Can prolonged exposure to information on COVID-19 affect mental health negatively?. Asian Population Studies 2023;19(1):40 View
  10. Joyce D, Kormilitzin A, Smith K, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability. npj Digital Medicine 2023;6(1) View
  11. Tong H, Hou W, Liang L, Li T, Liu H, Lee T, Stanley J. Age-Related Differences of Rumination on the Loneliness–Depression Relationship: Evidence From a Population-Representative Cohort. Innovation in Aging 2021;5(4) View
  12. Ćosić K, Popović S, Šarlija M, Kesedžić I, Gambiraža M, Dropuljić B, Mijić I, Henigsberg N, Jovanovic T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Frontiers in Psychology 2021;12 View
  13. Hill A, Joyner C, Keith-Jopp C, Yet B, Tuncer Sakar C, Marsh W, Morrissey D. Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study. JMIR Formative Research 2023;7:e44187 View
  14. Aguiar A, Bezerra A, Gaio R, Pinto M, Duarte R. Anxiety and Depression Symptoms During the COVID-19 Pandemic: A Cluster Analysis of Individuals Living in Portugal. Acta Médica Portuguesa 2023 View
  15. Sikström S, Kelmendi B, Persson N. Assessment of depression and anxiety in young and old with a question-based computational language approach. npj Mental Health Research 2023;2(1) View
  16. Han X, Hu N. Prediction of one- and three-months yoga practices effect on chronic venous insufficiency based on machine learning classifiers. Egyptian Informatics Journal 2024;27:100507 View
  17. Sun Y, Xu C, Xu H. Social identity in trusting artificial intelligence agents: Evidence from lab and online experiments. Managerial and Decision Economics 2024;45(8):5899 View

Books/Policy Documents

  1. Saha S, Murmu S, Manna S, Chowdhury B, Das N. Computational Intelligence in Pattern Recognition. View
  2. Lefèvre T, Colineaux H, Morgand C, Tournois L, Delpierre C. Artificial Intelligence in Covid-19. View
  3. Basu S, Shafi J. Principles and Methods of Explainable Artificial Intelligence in Healthcare. View
  4. Ganguly C, Nayak S, Gupta A. Artificial Intelligence, Machine Learning, and Mental Health in Pandemics. View