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.

Background: An ever increasing number of artificial intelligence (AI) models targeting healthcare applications are developed and published every day, but their use in real world decision making is limited. Beyond a quantitative assessment, it is important to have qualitative evaluation of the maturity of these publications with additional details related to trends in type of data used, type of models developed across the healthcare spectrum. Methods: We assessed the maturity of selected peer-reviewed AI publications pertinent to healthcare for 2019 to 2021. For the report, the data collection was performed by PubMed search using machine learning OR artificial intelligence AND Healthcare with the English language and human subject research as of December 31, each year. All three years selected were manually classified into 34 distinct medical specialties. We used the Bidirectional Encoder Representations from Transformers (BERT) neural networks model to identify the maturity level of research publications based on their abstracts. We further classified a mature publication based on the healthcare specialty and geographical location of the article's senior author. Finally, we manually annotated specific details from mature publications, such as model type, data type, and disease type. Results: Of the 7062 publications relevant to AI in healthcare from 2019 to 2021, 385 were classified as mature. In 2019, 6.01 percent of publications were mature. 7.7 percent were mature in 2020, and 1.81 percent of publications were mature in 2021. Radiology publications had the most mature model publications across all specialties over the last three years, followed by pathology in 2019, ophthalmology in 2020, and gastroenterology in 2021. Geographical pattern analysis revealed a non-uniform distribution pattern. In 2019 and 2020, the United States ranked first with a frequency of 22 and 50, followed by China with 20 and 47. In 2021, China ranked first with 17 mature articles, followed by the United States with 11 mature articles. Imaging based data was the primary source, and deep learning was the most frequently used modeling technique in mature publications. Interpretation: Despite the growing number of publications of AI models in healthcare, only a few publications have been found to be mature with a potentially positive impact on healthcare. Globally, there is an opportunity to leverage diverse datasets and models across the health spectrum, to develop more mature models and related publications, which can fully realize the potential of AI to transform healthcare.

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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.