IMR Press / JOMH / Special Issues / data_male_psychology

Analysis and Application of Data Mining and Multi-Attribute Decision-Making Methods in Male Psychology during Epidemic Normalization

Submission deadline: 28 February 2023
Special Issue Editors
  • Yen-Ching Chuang
    Institute of Public Health & Emergency Management, Taizhou University, Taizhou, Zhejiang, China; Business College, Taizhou University, Taizhou, Zhejiang, China
  • Ching-Wen Chien, PhD
    Institute for Hospital Management, Tsing Hua University, Shenzhen, Guangdong, China
  • Tao-Hsin Tung, PhD
    Evidence-based Medicine Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
  • James J.H. Liou
    Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
  • Chao Liu
    Institute for Hospital Management, Tsinghua University, Shenzhen, Guangdong, China
Special Issue Information

Dear Colleagues,

With the outbreak of COVID-19 in 2019, the world has stepped into the normalization of epidemic prevention and control. The epidemic has led to conditions such as increased stress in healthcare including difficulties and concerns in patient access to care. Long-term outbreaks may further lead to psychological trauma and burnout among healthcare providers. The life changes brought about by the epidemic further affect the psychological state of the population, such as health beliefs and perceptions of life. The psychological changes in health care and the population will in turn affect the effectiveness of epidemic prevention and control. In the real world, men may be less likely to express their psychological state (i.e. stress, burnout, and happiness). Therefore, psychological research on male healthcare personnel and the male population will help achieve better epidemic prevention and control, will provide a reference basis for healthcare managers to make decisions, and is a topic warranting further study.

With the continuous progress of information technology, many analytical methods can be applied to the study of complex medical problems. These include traditional statistical analysis methods, data-driven multi-attribute decision-making methods (data-driven MADM method) with data mining, expert knowledge, and data knowledge fusion. The effective integration of multiple aspects from different perspectives and the utilization of different methods can deepen the depth and breadth of research on the psychology of male health care and the public during epidemics, as well as promote innovation in the analysis of complex medical problems.

The purpose of this issue is to explore the use of data mining and statistical analysis to study the psychological changes and states of male healthcare personnel and the male population under the normalization of epidemics. Original research and review articles are welcome. Potential topics include, but are not limited to, the following:

Statistical analysis and assessment of the psychological state of male healthcare personnel and the male population during the presence of major infectious diseases.

Data-driven MADM method for psychological analysis of male healthcare personnel and the male population during epidemic situations.

Various data mining/machine learning/artificial intelligence models for the psychological study of male healthcare personnel and the male population during epidemic situations.

Psychological stress/burnout/happiness assessment and prevention strategies of male healthcare personnel and the male population in the prevention and control of major infectious disease outbreaks.

Dr. Yen-Ching Chuang, Prof. Ching-Wen Chien, Prof. Dr. Tao-Hsin Tung, Prof. James J.H. Liou and Mr. Chao Liu

Guest Editors

Keywords
psychological state
health care
major infectious diseases
data mining
statistical analysis
multi-attribute decision-making
data-driven multi-attribute decision-making
machine learning
artificial intelligence
Psychological stress
medical personnel and population
prevention and controal
Manuscript Submission Information

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