Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2021-04-01
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| Series: | JMIR Mental Health |
| Online Access: | https://mental.jmir.org/2021/4/e25097 |
| _version_ | 1818886356532723712 |
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| author | Jha, Indra Prakash Awasthi, Raghav Kumar, Ajit Kumar, Vibhor Sethi, Tavpritesh |
| author_facet | Jha, Indra Prakash Awasthi, Raghav Kumar, Ajit Kumar, Vibhor Sethi, Tavpritesh |
| author_sort | Jha, Indra Prakash |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T16:20:02Z |
| format | Article |
| id | doaj.art-45391bc076324f648e92744780bdac11 |
| institution | Directory Open Access Journal |
| issn | 2368-7959 |
| language | English |
| last_indexed | 2024-12-19T16:20:02Z |
| publishDate | 2021-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Mental Health |
| spelling | doaj.art-45391bc076324f648e92744780bdac112022-12-21T20:14:30ZengJMIR PublicationsJMIR Mental Health2368-79592021-04-0184e2509710.2196/25097Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational StudyJha, Indra PrakashAwasthi, RaghavKumar, AjitKumar, VibhorSethi, TavpriteshBackgroundThe 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.https://mental.jmir.org/2021/4/e25097 |
| spellingShingle | Jha, Indra Prakash Awasthi, Raghav Kumar, Ajit Kumar, Vibhor Sethi, Tavpritesh Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study JMIR Mental Health |
| title | Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study |
| title_full | Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study |
| title_fullStr | Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study |
| title_full_unstemmed | Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study |
| title_short | Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study |
| title_sort | learning the mental health impact of covid 19 in the united states with explainable artificial intelligence observational study |
| url | https://mental.jmir.org/2021/4/e25097 |
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