The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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A Semantic Diagnosis and Tracking System to Prevent the Spread of COVID-19

COVID-19 확산 방지를 위한 시맨틱 진단 및 추적시스템

  • 순위샹 (경북대학교 IT대학 컴퓨터학부) ;
  • 이용주 (경북대학교 IT대학 컴퓨터학부)
  • Received : 2020.04.09
  • Accepted : 2020.06.15
  • Published : 2020.06.30
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Abstract

In order to prevent the further spread of the COVID-19 virus in big cities, this paper proposes a semantic diagnosis and tracking system based on Linked Data through the cluster analysis of the infection situation in Seoul, South Korea. This paper is mainly composed of three sections, information of infected people in Seoul is collected for the cluster analysis, important infected patient attributes are extracted to establish a diagnostic model based on random forest, and a tracking system based on Linked Data is designed and implemented. Experimental results show that the accuracy of our diagnostic model is more than 80%. Moreover, our tracking system is more flexible and open than existing systems and supports semantic queries.

본 논문은 대도시에서의 COVID-19 바이러스 확산을 막기 위해, 대한민국 서울의 감염 상황에 대한 클러스터 분석을 통한 링크드 데이터 기반 시맨틱 진단 및 추적 시스템을 제안한다. 본 논문은 크게 3개의 섹션으로 구성되어 있는데, 클러스터 분석을 위해 서울의 감염자 정보를 수집하고, 중요한 감염 환자 속성을 추출하여 랜덤 포레스트를 기반으로 한 진단 모델을 구축하고, 그리고 링크드 데이터를 기반으로 한 추적 시스템을 설계하고 구현한다. 실험 결과 진단 모델의 정확도가 80% 이상으로 나타났으며, 더군다나 본 논문에서 제안한 추적 시스템은 기존 시스템들보다 더 유연하고 개방적이며 시맨틱 쿼리도 지원한다.

Keywords

References

  1. COVID-19, World Health Organization, 2020.
  2. COVID-19, Cable News Network, 2020.
  3. H. S Seok and Y. Lee, "Ontology-based IoT Context Information Modeling and Semanticbased IoT Mashup Services Implementation," J. of the Korea Institute of Electronic Communication Sciences, vol. 14, no. 4, 2019, pp. 71-76.
  4. C. W Kim and J. W Kim, "Image Retrieval System of semantic Inference using Objects in Images," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 7, 2019, pp. 677-684. https://doi.org/10.13067/JKIECS.2016.11.7.677
  5. Y. X Sun and Y. Lee, "Storage and Retrieval Architecture based on Key-Value Solid State Device," J. of the Korea Institute of Electronic Communication Sciences, vol. 15, no 1, 2020, pp. 45-52. https://doi.org/10.13067/JKIECS.2020.15.1.45
  6. I. Kononenko, "Machine learning for medical diagnosis: History, state of the art and perspective," J. of the Artificial Intelligence in Medicine, vol. 23, 2001, pp. 89-109. https://doi.org/10.1016/S0933-3657(01)00077-X
  7. W. Raghupathi and V. Raghupathi, "Big data analytics in healthcare: promise and potential. Commun," J. of the Health Information Science and Systems. vol. 2, no 3, 2012, pp. 11-13.
  8. Z. S.Y. Wong, J. Zhou, and Q. Zhang, "Artificial Intelligence for infectious disease Big Data Analytics," J. of Infection, Disease and Health. vol. 24, 2019, pp. 44-48. https://doi.org/10.1016/j.idh.2018.10.002
  9. V. Gianfredi1, N. L. Bragazzi, D. Nucci, M. Martini, R. Rosselli, L. Minelli, and M. Morettiac, "Harnessing big data for communicable tropical and subtropical disorders Implications from a systematic review of the literature," J. of Frontiers in Public Health, vol. 6, no. 90, 2018, pp. 1-15. https://doi.org/10.3389/fpubh.2018.00001
  10. Y. Wang , Y. Fan, P. Bhatt, and C. Davatzikos, "High-dimensional pattern regression using machine learning," From medical images to continuous clinical variables. J. of Neuroimage, vol. 50, 2010, pp. 1519-1535. https://doi.org/10.1016/j.neuroimage.2009.12.092
  11. S. Schneeweiss, "Learning from Big Health Care Data," The New England J. of Medicine, vol. 370, no. 23, 2014, pp.2161-2163. https://doi.org/10.1056/NEJMp1401111
  12. K. E. Goodman, J. Lessler, S. E. Cosgrove, A. D. Harris, E. Lautenbach, J. H. Han, A. M. Milstone, C. J. Massey, and P. D. Tamma, "A Clinical Decision Tree to Predict Whether a Bacteremic Patient is Infected with an Extended-Spectrum ${\beta}$-Lactamase-Producing Organism," J. of Clinical Infectious Diseases, vol. 63, 2016, pp.896-903. https://doi.org/10.1093/cid/ciw425
  13. K. Tsui and Z. Shui-Yee. Wong, "Tracking infectious disease spread for global pandemic containment," J. of IEEE Intelligent Systems, vol. 28, 2013, pp.60-64.
  14. J. Mossong, N. Hens, M. Jit, P. Beutels, K. Auranen, R. Mikolajczyk, M. Massari, S. Salmaso, G. Scalia Tomba, J. W. J. Heijne, M. Sadkowska-Todys, M. Rosinska, and W. J. Edmunds, "Social contacts and mixing patterns relevant to the spread of infectious diseases," J. of PLoS Medicine, vol. 5, 2008, pp.0381-0391.
  15. Q. Tan, J. Liu, B. Shi, Y. Liu, and X. Zhou, "Public health surveillance with incomplete data - Spatio-temporal imputation for inferring infectious disease dynamics," In Proc. 2018 IEEE International Conference on Healthcare Informatics, New York City, USA, 2018. pp. 255-264.
  16. C. L. Moyes, W. H. Temperley, A. J. Henry, C. R. Burgert, and S. I. Hay, "Providing open access data online to advance malaria research and control," J. Malaria, vol. 12, 2013, pp. 1-9. https://doi.org/10.1186/1475-2875-12-1
  17. J. Wan and C. Zou, "Cloud-Enabled wireless body area networks for pervasive healthcare," J. of IEEE Network, vol. 27, 2013, pp.56-61.