디지털융복합연구 (Journal of Digital Convergence)

  • 제15권3호
  • /
  • Pages.221-228
  • /
  • 2017
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

헬스케어 정보 수집을 위한 딥 러닝 기반의 서브넷 구축 기법

Subnet Generation Scheme based on Deep Learing for Healthcare Information Gathering

  • 정윤수 (목원대학교 정보통신융합공학부)
  • Jeong, Yoon-Su (Dept. of Information Communication & Engineeringe, Mokwon University)
  • 투고 : 2016.12.21
  • 심사 : 2017.03.20
  • 발행 : 2017.03.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 IoT 기술이 발달함에 따라 헬스케어 서비스를 제공하는 많은 의료기관에서는 IoT 기술을 이용한 의료 서비스가 점점 증가하고 있는 추세이다. 그러나, 사용자 신체에 부착된 IoT 센서의 갯수가 증가하면서 서버에 전달되는 헬스케어 정보는 점점 복잡해져서 서버에서 사용자의 헬스케어 정보를 분석하는 시간이 증가하는 문제점이 발생하고 있다. 본 논문에서는 사용자에 부착된 IoT 장치를 통해 전달되는 다량의 헬스케어 정보를 서버에서 의료 목적에 맞게 헬스케어 정보를 수집 및 처리하기 위한 딥 러닝 기반의 헬스케어 정보 관리 기법을 제안한다. 제안 기법은 서버에 전달된 헬스케어 정보에 속성값을 부여하여 속성값에 따른 서브넷을 구축한 후 서브넷간 연계 정보를 시드로 추출하여 계층적 구조로 그룹화 한다. 제안 기법은 서버에서 그룹화된 헬스케어 정보를 딥 러닝에 적용하여 사용자의 치료 및 처방에 대한 관찰 속도 및 정확도를 향상시킬 수 있어 최적화된 정보를 추출할 수 있는 특징이 있다. 성능평가 결과, 제안기법은 헬스케어 서비스 모델에서 동작되는 의료 서비스의 처리속도가 기존기법에 비해 평균 14.1% 향상되었고, 서버의 오버헤드는 기존 기법보다 평균 6.7% 낮았다. 헬스케어 정보 추출에 대한 정확도는 기존 기법보다 평균 10.1% 높은 결과를 얻었다.

With the recent development of IoT technology, medical services using IoT technology are increasing in many medical institutions providing health care services. However, as the number of IoT sensors attached to the user body increases, the healthcare information transmitted to the server becomes complicated, thereby increasing the time required for analyzing the user's healthcare information in the server. In this paper, we propose a deep learning based health care information management method to collect and process healthcare information in a server for a large amount of healthcare information delivered through a user - attached IoT device. The proposed scheme constructs a subnet according to the attribute value by assigning an attribute value to the healthcare information transmitted to the server, and extracts the association information between the subnets as a seed and groups them into a hierarchical structure. The server extracts optimized information that can improve the observation speed and accuracy of user's treatment and prescription by using deep running of grouped healthcare information. As a result of the performance evaluation, the proposed method shows that the processing speed of the medical service operated in the healthcare service model is improved by 14.1% on average and the server overhead is 6.7% lower than the conventional technique. The accuracy of healthcare information extraction was 10.1% higher than the conventional method.

키워드

참고문헌

  1. V. Gadepally, J. Kepner. "Big data dimensional analysis", 2014 IEEE High Performance Extreme Computing Conference(HPEC) pp. 1-6, Sep. 2014.
  2. Y. Demchenko, C. De Laat, P. Membrey, "Defining architecture components of the Big data Ecosystem", 2014 International conference on Collaboration Technologies and Systems(CTS), pp.104-112, May, 2014.
  3. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker and I. Stoica, "Spark: cluster computing with working sets," Proceedings of the 2nd USENIX conference on Hot topics in cloud computing (HotCloud'10), pp. 10-10, Jun. 2010.
  4. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker and I. Stoica, "Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing", Proceeding of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI'12), pp. 2-2, April. 2012.
  5. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. H. Byers, "Big Data: The Next Frontier for Innovation, Competition and Productivity", Mckinsey Global Institute, pp. 1-137. 2011.
  6. W. Yan, U. Brahmakshatriya, Y. Xue, M. Gilder and B. Wise, "p-Pic: Parallel Power iteration clustering for big data", Journal of Parallel and Distributed computing, Vol. 73, No. 3, pp. 353-359, Mar. 2013.
  7. X. Cui, J. S. Charles and T. Potok, "GPU enhanced parallel computing for large scale data clustering", Journal of Future Generation Computer Systems, Vol. 29, No. 7, pp. 1736-1741, Sep. 2013. https://doi.org/10.1016/j.future.2012.07.009
  8. G. Andrade, G. Ramos, D. Madeira, R. Sachetto, R. Ferreira and L. Rocha, "G-dbscan: A gpu accelerated algorithm for density based clustering", Journal of Procedia Computer Science, Vol. 18, pp. 369-378, Aug. 2013. https://doi.org/10.1016/j.procs.2013.05.200
  9. H. Karau, A. Konwinski, P. Wendell and M. Zaharia, "Learning Spark: Lightning-Fast Big Data Analysis", O'Reilly Media, Inc. pp. 1-276, Jan. 2015.
  10. A. Katal, M. Wazid, R. H. Goudar, "Big data: Issues, challenges, tools and Good practices ", 2013 Sixth International Conference on Contemporary Computing(IC3), pp. 404-409, Aug. 2013.
  11. X. Cui, P. Zhu, X. Yang, K. Li and C. Ji, "Optimized big data K-means clustering using MapReduce", Journal of Supercomputing, Vol. 70, Issue 3, pp. 1249-1259, Dec. 2014. https://doi.org/10.1007/s11227-014-1225-7
  12. W. Zhao, H. Ma and Q. He, "Parallel k-means clustering based on mapreduce", Proceedings of the 1st International Conference on Cloud Computing, pp. 674-679, Dec. 2009.
  13. B. Bahmani, B. Moseley, A. Vattani, R. Kumar and S. Vassivitskii, "Scalable k-means++", Proceedings of the VLDB Endowment, Vol. 5, No. 7, pp. 622-633, 2012.
  14. X. Cai, F. Nie and H. Huang, "Multi-view k-means clustering on big data", Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, AAAI Press, pp. 2598-2604, 2013.
  15. Y. H. Kim, K S. Shim, M. S. Kim and J. S. Lee, "DBCURE-MR: an efficient density-based clustering algorithm for large data using MapReduce", Journal of Information Systems, Vol. 42, pp. 15-35, Jun. 2014. https://doi.org/10.1016/j.is.2013.11.002
  16. Y. S. Jeong, "An Efficiency Management Scheme using Big Data of Healthcare Patients using Puzzy AHP," Journal of Digital Convergence, Vol. 13, No. 4, pp. 227-234, Apr. 2010. https://doi.org/10.14400/JDC.2015.13.4.227
  17. Y. S. Jeong, "Design of Prevention Model according to a Dysfunctional of Corporate Information," Journal of Convergence Soceity for SMB, Vol. 6, No. 2, pp. 11-17, Jun. 2016. https://doi.org/10.22156/CS4SMB.2016.6.2.011
  18. Y. S. Jeong, "Tracking Analysis of User Privacy Damage using Smartphone", Journal of Convergence Society for SMB, Vol. 4, No. 4, Dec. 2014.
  19. Y. S. Jeong, "Design of Security Model for Service of Company Information," Journal of Convergence Soceity for SMB, Vol. 2, No. 2, pp. 43-49, Nov. 2012.
  20. S. H. Lee and D. W. Lee, "A Study on u-Health Fusion Field based on Internet of Thing," Journal of the Korea Convergence Society, Vol. 7, No. 4, pp. 19-24, Aug. 2016. https://doi.org/10.15207/JKCS.2016.7.4.019
  21. G. W. Jin, "A Study on the Data Colection Methods based Hadoop Distributed Environment," Journal of the Korea Convergence Society, Vol. 7, No. 5, pp. 1-6, Oct. 2016. https://doi.org/10.15207/JKCS.2016.7.5.001