Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Wang X

PROVIDER: S-EPMC8452905 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Wang Xiaofeng X Li Hu H Sun Chuanyong C Zhang Xiumin X Wang Tan T Dong Chenyu C Guo Dongyang D

Frontiers in public health 20210907

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 o ...[more]

PMID: 34557468

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Project description:ObjectivesThis study aimed to measure the prevalence of burnout syndrome, anxiety, depression, and post-traumatic disorders (PTSD), as well as examine their associated factors among Thai healthcare workers (HCWs) during COVID-19 outbreak.MethodWe employed a multiple-method design at a tertiary-care hospital in Bangkok between May 22, 2021 and June 30, 2021 by using an online survey. The information included demographic characteristics, work details, perceived support, PTSD symptoms, Maslach Burnout Inventory: General Survey (MBI-GS), General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire (PHQ-2 and PHQ-9), and narrative response to an open-ended question. The associated factors of mental health problems were analysed by multiple logistic regression analyses. The qualitative data were analysed by the content analysis method.ResultsA total of 986 HCWs (89.1% female; mean age = 34.89 ± 11.05 years) responded to the survey. 16.3%,16%, and 53.5% of respondents had a high level of emotional exhaustion, depersonalisation, and diminished personal achievement, respectively. 33.1%, 13.8%, and 2.3% of respondents had anxiety, depression, and PTSD. Risk factors of emotional exhaustion were male sex (ORadj = 2.29), nurses (ORadj = 3.04), doctors (ORadj = 4.29), working at COVID-19 inpatient unit (ORadj = 2.97), and working at COVID-19 intensive care unit (ORadj = 3.00). Additionally, preexisting mental illness was associated with anxiety (ORadj = 2.89), depression (ORadj = 3.47), and PTSD (ORadj = 4.06). From qualitative analysis, participants reported that these factors would improve their mental health: supportive and respectful colleagues, appropriate financial compensation, reduced workload, clarity of policy and communication channel, and adequate personal protective equipment.ConclusionsThai HCWs experienced negative mental health outcomes during the COVID-19 pandemic substantially. This issue needs attention and actions should be implemented to support them.

Project description:IntroductionThe coronavirus disease 2019 (COVID-19) pandemic caused significant changes in the everyday functioning of the general population, as well as medical workers. Medical personnel, especially those in direct contact with COVID-19 patients, could have increased levels of stress, anxiety, and depression. The objective of this study was to explore the mental health status of medical personnel in Serbia during the pandemic by assessing stress levels, symptoms of anxiety, and depression.MethodsThis cross-sectional study was conducted as an online-based survey, in the period from 8 April to 14 April 2020, during the COVID-19 pandemic. The study included 1678 participants, and the snowball sampling technique was used to reach healthcare professionals. The level of stress and symptoms of depression and anxiety were assessed among medical personnel in Serbia by the 10-item Perceived Stress Scale (PSS), the Beck Depression Inventory IA (BDI-IA), and the 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively.ResultsA total of 1678 participants completed the survey, with a mean age of 40.38 ± 10.32 years, of which 1,315 (78.4%) were women, and 363 (21.6%) were men. Out of these, 684 (40.8%) participants were medical personnel, and 994 (59.2%) were people of other professions. Frontline medical personnel reported higher scores on all measurement tools than second-line medical personnel (e.g., mean PSS scores: 19.12 ± 5.66 versus 17.53 ± 5.71; p = .006; mean GAD-7 scores: 8.57 ± 6.26 versus 6.73 ± 5.76; p = .001; mean BDI-IA scores: 9.25 ± 8.26 versus 7.36 ± 7.28; p = .006). Binary logistic regression showed that the probability of developing more severe anxiety symptoms doubles in frontline medical personnel.ConclusionOur findings suggest that frontline medical personnel is under an increased psychological burden during the COVID-19 pandemic, having higher levels of stress, anxiety, and depression than second-line medical personnel. Adequate measures should be taken to relieve this burden and preserve the mental health of frontline medical personnel.

Dataset Information

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

Publications

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

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