Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.

Mental health prediction is one of the most essential parts of reducing the probability of serious mental illness. Meanwhile, mental health prediction can provide a theoretical basis for public health department to work out psychological intervention plans for medical workers. The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. Besides, we use stepwise logistic regression, binary bat algorithm, hybrid improved dragonfly algorithm and the proposed prediction model to predict mental health of medical workers. The results show that the prediction accuracy of the proposed model is 92.55%, which is better than the existing algorithms. This method can be used to predict mental health of global medical worker. In addition, the method proposed in this paper can also play a role in the appropriate work plan for medical worker.

Experience of and Worry About Discrimination, Social Media Use, and Depression Among Asians in the United States During the COVID-19 Pandemic: Cross-sectional Survey Study.

BACKGROUND: The COVID-19 outbreak has spurred increasing anti-Asian racism and xenophobia in the United States, which might be detrimental to the psychological well-being of Asian people living in the United States. OBJECTIVE: We studied three discrimination-related variables, including (1) experience of discrimination, (2) worry about discrimination, and (3) racism-related social media use during the COVID-19 pandemic among Asians in the United States. We examined how these three variables were related to depression, and how the association between racism-related social media use and depression was moderated by personal experience of and worry about racial discrimination. METHODS: A web-based, cross-sectional survey was conducted. A total of 209 people (mean age 33.69, SD 11.31 years; 96/209, 45.93% female) who identified themselves as Asian and resided in the United States were included in the study. RESULTS: Experience of discrimination (ß=.33, P=.001) and racism-related social media use (ß=.14, P=.045) were positively associated with depressive symptoms. Worry about discrimination (ß=.13, P=.14) was not associated with depression. Worry about discrimination moderated the relationship between racism-related social media use and depression (ß=-.25, P=.003) such that a positive relationship was observed among those who had low and medium levels of worry. CONCLUSIONS: The present study provided preliminary evidence that experience of discrimination during the COVID-19 pandemic was a risk factor of depressive symptoms among Asian people in the United States. Meanwhile, racism-related social media use was found to be negatively associated with the well-being of US Asians, and the relationship between social media use and depression was significantly moderated by worry about discrimination. It is critical to develop accessible programs to help US Asians cope with racial discrimination both in real lives and on social media during this unprecedented health crisis, especially among those who have not been mentally prepared for such challenges.