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

Abstract

Background The 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. Objective This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic. Methods In 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. Results Overall, 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. Conclusions Multiple 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.

Effects of the COVID-19 Pandemic on Psychological Well-Being and Mental Health Based on a German Online Survey

“…), and psychological distress (PHQ-4). Contrary to previous studies reporting a higher degree of psychological stress in women ( 16 ), women did not predominantly belong to the concerned cluster in our sample. However, the difference in comparison with other studies might be explained by the rather small sample size and selection bias due to convenience sampling.…”

“…Our study replicated previously reported results [e.g., ( 4 , 9 , 16 – 18 )] of lower psychological well-being and mental health during the COVID-19 pandemic in (i) women weakened in their psychological factors (supported by the regression analyses of the concerned cluster), (ii) persons weakened in their psychological factors and with higher risk factors, and (iii) younger persons. Our approach could identify individuals vulnerable in their psychological well-being and mental health and key factors influencing the psychological well-being during the COVID-19 pandemic.…”

“…In addition, values of the established General Health Questionnaire (GHQ-12) increased from 2018/2019 to April 2020 indicating more stress and lower mental health. The changes in mental health were particularly noticeable among women [cf., ( 16 )], younger individuals between 18 and 34 years [cf., ( 14 , 16 , 17 )], and persons living with young children ( 18 ). Another study investigated the psychological impact of COVID-19 cross-sectionally in China at an early stage of the pandemic using survey data from 1,210 participants ( 4 ).…”

“…Analyzing posts in the Chinese online social network Weibo, Li et al ( 22 ) observed increased negative emotions and sensitivity to social risks with decreased positive emotions and life satisfaction. Jha et al ( 16 ) combined Bayesian networks and ML methods on a representative sample of 17,764 adults in the USA to identify key factors for mental health during the COVID-19 pandemic. They were able to predict the level of mental health of each individual with an average accuracy of ~80%.…”

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients

“…Explainability, as a key prerequisite for the clinical use of the developed ML methods ( Tjoa and Guan, 2020 ), is important for reliable prediction, but also for personalized treatment and prevention of mental health deterioration in ex-COVID-19 patients. When mentioning explainability, people often refer to global explainability ( Jha et al, 2021 ), which is useful for understanding the underlying mechanisms of mental health deterioration, e.g., what variables contribute most significantly to a given prediction. Besides such global explanations, in the context of health applications, the local explainability of the developed models is crucial, i.e., providing the information to the end user, in this case the mental health practitioners, on why a particular decision was made.…”