Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

The Classification System and Information Service for Establishing a National Collaborative R&D Strategy in Infectious Diseases: Focusing on the Classification Model for Overseas Coronavirus R&D Projects

국가 감염병 공동R&D전략 수립을 위한 분류체계 및 정보서비스에 대한 연구: 해외 코로나바이러스 R&D과제의 분류모델을 중심으로

  • Lee, Doyeon (Division of Data Analysis, Korea Institute of Science and Technology Information) ;
  • Lee, Jae-Seong (Department of Science and Technology Management Policy, University of Science and Technology) ;
  • Jun, Seung-pyo (Data Analysis Platform Center, Korea Institute of Science and Technology Information) ;
  • Kim, Keun-Hwan (Division of Data Analysis, Korea Institute of Science and Technology Information)
  • 이도연 (한국과학기술정보연구원 데이터분석본부) ;
  • 이재성 (과학기술연합대학원대학교 과학기술경영정책학과) ;
  • 전승표 (한국과학기술정보연구원 데이터분석플랫폼센터) ;
  • 김근환 (한국과학기술정보연구원 데이터분석본부)
  • Received : 2020.08.18
  • Accepted : 2020.09.18
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

세계는 신형 코로나바이러스 감염증(COVID-19)으로 수 많은 인명 피해와 경제적 손실을 기록하고 있는 상황이다. 우리나라 정부는 연구개발(Research & Development)을 통해 국가 감염병 위기를 극복하려는 전략을 수립하고 실행하기 위한 투자방향을 수립하였다. 기존 기술분류나 과학기술 표준분류에 따른 통계를 활용하면 특정 R&D 분야의 특이점 및 변화를 발견하기 어렵다는 한계가 존재해왔다. 최근 우리나라 감염병 연구개발 과제를 대상으로 수요자의 목적에 맞게 분류체계를 수립하고 연구비 비교 분석을 통해 투자가 요구되는 연구 분야를 제시하는 연구들이 진행되었다. 하지만 현재 국가 보건 안보와 신성장 산업육성이라는 목표를 달성하기 위한 실행방안으로 요구되고 있는 전염병 연구분야의 국가간 협력전략 수립에 필요한 정보를 체계적으로 제공하고 있지 못한 상황이다. 따라서 국가 공동 연구개발 전략 수립을 위한 분류체계와 분류모델기반의 정보서비스에 대한 연구가 요구되고 있다. 우선 감염병관련 NTIS 과제데이터를 기반으로 정성분석을 통해 7개의 분류체계를 도출하였다. 스코퍼스(Scopus) 데이터와 양방향 RNN모델을 사용하여, 분류체계 모델을 학습시켰다. 최종적인 모델의 분류 성능은 90%이상의 높은 정확도와 강건성을 확보하였다. 실증연구를 위해 주요 국가의 코로나바이러스 연구개발 과제를 대상으로 전염병 분류체계를 적용하였다. 주요 국가의 감염병(코로나바이러스) 연구개발 과제를 분류체계별로 분석한 결과, 세계적으로 유행하는 바이러스의 예상치 못한 창궐이 확산되는 속도에 비해 백신과 치료제 개발이 제대로 이뤄지지 않는 원인의 배경을 간접적으로 확인할 수 있었다. 국가별 비교분석을 통해 미국과 일본은 상대적으로 모든 영역에 골고루 연구개발 투자를 하고 있는 것으로 나타난 반면, 유럽은 상대적으로 특정 연구분야에 많은 투자를 하는 집중화 전략을 취하는 것으로 나타났다. 동시에 주요 국가의 코로나 바이러스 주요 연구조직에 대한 정보를 분류체계별로 제공하여 국제 공동R&D 전략의 기초정보를 제공하였다. 본 연구 결과를 통해 세 가지 정책적 의미를 도출할 수 있다. 첫째, 데이터기반 과학기술정책 관점에서 수요자 관심분야에 대한 국가 R&D사업의 정보를 글로벌 기준으로 문서를 분류하는 방안을 제시하였다. 둘째, 감염병관련 국가 R&D사업 영역에 대한 정보분석 서비스 기획의 기반을 마련하였다. 마지막으로 국가 감염병 R&D 분류체계 수립을 통해 분류 체계의 궁극적 목표인 산업, 기업, 정책 정보를 제공할 수 있는 기반을 마련한 것이다.

Keywords

References

  1. Chen, T.-K., H.-H. Liao, and H.-J. Kuo, "Internal liquidity risk, financial bullwhip effects, and corporate bond yield spreads: Supply chain perspectives", Journal of Banking & Finance, Vol.37, No.7(2013), 2434-2456. https://doi.org/10.1016/j.jbankfin.2013.02.011
  2. Foltz, P.-W., and S.-T. Dumais, "Personalized information delivery: An analysis of information filtering methods", Communications of the ACM, Vol.35, No.12(1992), 51-60. https://doi.org/10.1145/138859.138866
  3. Hankookilbo, Live Issue, 2020. Available at https://www.hankookilbo.com/News/Read/A2020081406480005122 (Access 15 August, 2020).
  4. Hong, S-K, "Research on classification criteria for national R&D projects for systematic information provision", Research Report, Korea Institute of Science and Technology Evaluation and Planning, 2016.
  5. Houseman, E.-M., and D.-E. Kaskela, "State of the art of selective dissemination of information", IEEE Trans. Eng. Writing Speech III, (1970), 78-83.
  6. Jeong, H., Y.-K. Kim, and J. Kim, "An evaluationcommittee recommendation system for national R&D projects using social network analysis", Cluster Computing, Vol.19, No.2(2016), 921-930. https://doi.org/10.1007/s10586-016-0545-1
  7. Joseilbo, Economic news, 2020. Available at http://www.joseilbo.com/news/htmls/2020/07/20200723402630.html (Access 15 August, 2020).
  8. Kwak S.-J., H.-Y. Lee, "A Study on Classification and Metadata for R&D Reports in the Field of Energy", Annals of Social Science, Vol.24, No.2(2013), 361-378.
  9. Kim J., C. Yeo, "New infectious disease crisis response technology (diagnosis, treatment, vaccine)", Research Report, Korea Advanced Institute of Science and Technology Evaluation, 2020.
  10. Kim J.-W., H.-I. Jo, B.-G. Lee, "A Comparison Study on Performance of Malicious Comment Classification Models Applied with Artificial Neural Network", Journal of Digital Contents Society, Vol. 20, No. 7(2019), 1429-1437. https://doi.org/10.9728/dcs.2019.20.7.1429
  11. Kim S., C. Kim, H. Jang, S. Ye, M. Song, "A Classification for Research Projects in Oriental Medicine Field", Journal of the Korean society for information management, Vol.25,No.4(2008), 309-326. https://doi.org/10.3743/KOSIM.2008.25.4.309
  12. Korea Centers for Disease Control and Prevention, "Implementing national emergency response strategy: A study on R&D execution plan and new project planning". 2020.
  13. Korea Health Industry Development Institute, "New Convergence Industry Discovery Research (Convergence Technology): Classification System Study of HT Convergence Technology". 2015.
  14. Lee, D, J. Kang, K. Kim, "Global collaboration research strategies for sustainability in the post COVID-19 era: analyzing virology-related national-funded projects", Sustainability, Vol.12(2020), 6561. https://doi.org/10.3390/su12166561
  15. Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B. J., Wong, K. F., and M. Cha, "Detecting rumors from microblogs with recurrent neural networks", Proceedings of the 25th International Joint Conference on Artificial Intelligence, (2016), 3818-3824.
  16. Mikolov, T., M. Karafiat, L. Burget, J. Cernocky, and S. Khudanpur, "Recurrent neural network based language model", INTERSPEECH 2010 11th Annual Conference of the International Speech Communication Association, (2010), 1045-1048.
  17. Ministry of Economy and Finance, "Held the 3rd Emergency Economy Central Countermeasure Headquarters Meeting (Infectious Disease Response Industry Promotion Plan)", 2020. Available at http://www.moef.go.kr/nw/nes/detailNesDtaView.do?searchBbsId=MOSFBBS_000000000028&searchNttId=MOSF_000000000036711&menuNo=4010100 (Access 15 August, 2020).
  18. Ministry of Health and Welfare, "A Study on the Research Plan for the Drive Plan of Multi-Agency R&D Program to Implement the National Responding strategy of Infectious Diseases", 2019.
  19. Ministry of Science and ICT, "2021 Government R&D investment direction and standard revision", 2020.
  20. Moon, S.-Y, "Classification scheme of biotechnology R&D for strategic budget allocation", Research Report, Korea Institute of Science and Technology Evaluation and Planning, 2015.
  21. Sebastiani, F., "Machine learning in automated text categorization", ACM Computing Surveys, Vol.34, No.1(2002), 1-47. https://doi.org/10.1145/505282.505283
  22. Seo, T. and B. Kim, "Understanding classification, thesaurus, and ontology for information services", Research Report, Korea Institute of Science and Technology Information, 2015.
  23. Seok, K., "Smoothing parameter selection in semi-supervised learning", Journal of the Korean Data & Information Science Society, Vol.27, No.4(2016), 993-1000. https://doi.org/10.7465/jkdi.2016.27.4.993
  24. Sohn, S.-H., and B.-K. Yoo, "New drug classification system in accordance with global harmonization", Korean Journal of Clinical Pharmacy, Vol.22, No.3(2012), 260-267.
  25. Tang, J., S. Wu, J. Sun, and H. Su, "Cross-domain collaboration recommendation", Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, (2012), 1285-1293.
  26. Tian, Q., J. Ma, J. Liang, R.-C. Kwok, and O. Liu, "An organizational decision support system for effective R&D project selection", Decision support systems, Vol.39, No.3(2005), 403-413. https://doi.org/10.1016/j.dss.2003.08.005
  27. Trappey, A.-J., C.-V. Trappey, C.-Y. Wu, C.-Y. Fan, and Y.-L. Lin, "Intelligent patent recommendation system for innovative design collaboration", Journal of Network and Computer Applications, Vol.36, No.6(2013), 1441-1450. https://doi.org/10.1016/j.jnca.2013.02.035
  28. Xu, W., J. Sun, J. Ma, and W. Du, "A personalized information recommendation system for R&D project opportunity finding in big data contexts", Journal of Network and Computer Applications, Vol.59 (2016), 362-369. https://doi.org/10.1016/j.jnca.2015.01.003
  29. Yang, Y, "An evaluation of statistical approaches to text categorization", Inform. Retr. Vol.1(1999), 69-90. https://doi.org/10.1023/A:1009982220290