ArticleJMIR mental health2021
Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study.
Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Financial Disruption and Psychological Underpinning During COVID-19: A Review and Research Agenda.Frontiers in psychology · 2022Pooled it
- Uncovering hidden and complex relations of pandemic dynamics using an AI driven system.Scientific reports · 2024Article
- Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study.JMIR formative research · 2023Article
- Assessment of depression and anxiety in young and old with a question-based computational language approach.Npj mental health research · 2023Article
- The traumatic impact of the COVID-19 pandemic: the possible role of rumination and uncertainty.Current psychology (New Brunswick, N.J.) · 2023Article
- Explainable artificial intelligence for mental health through transparency and interpretability for understandability.NPJ digital medicine · 2023Review
- Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms.Procedia computer science · 2023Article
- Learning Agility of Learning and Development Professionals in the Life Sciences Field During the COVID-19 Pandemic: Empirical Study.Interactive journal of medical research · 2022Article
- Method for Data Quality Assessment of Synthetic Industrial Data.Sensors (Basel, Switzerland) · 2022Article
- Analysis of the Effects of Arts and Crafts in Public Mental Health Education Based on Artificial Intelligence Technology.Journal of environmental and public health · 2022Article
- Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.Informatics in medicine unlocked · 2022Article
- Article
- Effects of the COVID-19 Pandemic on Psychological Well-Being and Mental Health Based on a German Online Survey.Frontiers in public health · 2021Article
- AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients.Frontiers in psychology · 2021Review
- Age-Related Differences of Rumination on the Loneliness-Depression Relationship: Evidence From a Population-Representative Cohort.Innovation in aging · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being.
objectiveThis study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic.
methodsIn this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence.
resultsOverall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19-generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy.
conclusionsMultiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.
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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.