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Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study
JMIR Mental Health ( IF 5.2 ) Pub Date : 2021-04-20 , DOI: 10.2196/25097
Indra Prakash Jha , Raghav Awasthi , Ajit Kumar , Vibhor Kumar , Tavpritesh Sethi

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.

中文翻译:

通过可解释的人工智能了解美国对COVID-19的心理健康影响:观察性研究

背景: COVID-19大流行已经影响了全球许多国家的健康,经济和社会结构。识别个人水平的易感性因素可以帮助人们识别和管理其情绪,心理和社会福祉。目的:本研究的重点是学习可能表明在COVID-19大流行期间易患精神障碍的因素的分级列表。方法:在这项研究中,我们对美国不同年龄组,性别和社会经济地位的17,764名成年人进行了调查。通过初步的统计分析和贝叶斯网络推断,我们确定了影响COVID-19大流行期间心理健康的关键因素。贝叶斯网络与经典机器学习方法的集成导致了心理健康患病水平的有效建模。结果:总体而言,女性比男性承受更大的压力,并且18-29岁年龄段的人比其他年龄段的人更容易焦虑。使用贝叶斯网络模型,我们发现患有慢性精神疾病的人在COVID-19大流行期间更容易患精神疾病。在家工作的新现实;家庭教育;缺乏与家人,朋友和邻居的沟通会引起精神压力。在COVID-19引发的经济危机期间,社会保障提供的财政援助有助于减轻精神压力。最后,使用监督式机器学习模型,我们以〜80%的准确性预测了最易受精神困扰的人。结论:在COVID-19大流行期间,社会隔离,数字通信以及在家工作和上学等多重因素被确定为精神疾病的因素。与朋友和家人进行定期的面对面交流,健康的社会生活和社会保障是关键因素,在此期间,照顾有精神病史的人显得尤为重要。
更新日期:2021-04-20
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