A Study on Analysis of Risk Groups for Post-Traumatic Stress Disorder Based on NEMA Research Data
AUTHORS
JeongBeom Kim,Professor, Namseoul University, Korea
ABSTRACT
A typical mental problem that develops over a long period of time after experiencing a physically and mentally shocking event is known as post-traumatic stress disorder. Post-traumatic stress disorder is a complex psychological trauma that can lead to self-harm and self-harm and suicidal thoughts at risk, even in serious cases to suicide. The root cause of this phenomenon can be found in many places, but repeated trauma exposure and inadequate medical support in regard to the occupational specificity of fire fighters can be cited as the main cause. As such, the psychological post-traumatic stress disorder needs to be dealt with as a mental health problem in the public domain, beyond the mental health level of the individual. Even for stability, research on advancement of analysis tools for high-risk groups and preemptive prediction methods should be carried out along with the shift of awareness of the actual risk factors in modern society to encompass social problems in a broad sense. The purpose of this study is to analyze the risk group of post-traumatic stress disorders exposed to various types of trauma such as accidents, damages and disasters based on NEMA (National Emergency Management Association) data.
KEYWORDS
PTSD (post-traumatic stress disorder), NEMA, Analysis, Cluster, Health Care
REFERENCES
[1] Bachlechner T., Majumder B. P., Mao H. H., Cottrell G. W., and McAuley J., “Rezero is all you need: Fast convergence at large depth,” arXiv preprint arXiv:2003.04887, (2020)
[2] Brock A., Donahue J., and Simonyan K., “Large scale: GAN training for high fidelity natural image synthesis,” In International Conference on Learning Representations, (2019)
[3] Singh S. and Krishnan S., “Filter response normalization layer: Eliminating batch dependence in the training of deep neural networks,” arXiv preprint arXiv:1911.09737, (2019)
[4] De S. and Smith S. L., “Batch: Normalization biases deep residual networks towards shallow paths,” arXiv preprint arXiv:2002.10444, (2020)
[5] “Big Data Computing Technology,” Hanbit Academy, pp.14-31, (2016)
[6] Real E., Liang C., So D. R., and Le Q. V., “Auto ML zero: Evolving machine learning algorithms from scratch,” arXiv preprint arXiv:2003.03384, (2020)
[7] Zoph B. and Le Q. V., “Neural architecture search with reinforcement learning,” In International Conference on Learning Representations, (2017)
[8] Real E., Aggarwal A., Huang Y., and Le Q. V., “Regularized evolution for image classifier architecture search,” In Proceedings of the AAAI conference on artificial intelligence, vol.33, pp.4780-4789, (2019) DOI: 10.1609/aaai.v33i01.33014780(CrossRef)(Google Scholar)
[9] Liu H., Simonyan K., Vinyals O., Fernando C., and Kavukcuoglu K., “Hierarchical: Representations for efficient architecture search,” In International Conference on Learning Representations, (2018)
[10] Karras T., Aila T., Laine S., and Lehtinen J., “Progressive growing of gans for improved quality, stability, and variation,” In International Conference on Learning Representations, (2018)