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Knowledge-based dynamic cluster model for healthcare management using a convolutional neural network

  • Kyungyong Chung
  • Hoill JungEmail author
Article
  • 1 Downloads

Abstract

Due to recent growing interest, the importance of preventive and efficient healthcare using big data scattered throughout various IoT devices is being emphasized in healthcare, as well in the IT field. The analysis of information in healthcare is mainly prediction using a user’s basic information and static data from a knowledge base. In this study, a knowledge-based dynamic cluster model using a convolutional neural network (CNN) is suggested for healthcare recommendations. The suggested method carries out a process to extend static data and a previous knowledge base from an ontology-based ambient-context knowledge base beyond knowledge-based healthcare management, which was the focus of previous study. It is possible to acquire and expand a large amount of high-quality information by reproducing inferred knowledge using a CNN, which is a deep-learning algorithm. A dynamic cluster model is developed, and the accuracy of the predictions is improved in order to enable recommendations on healthcare according to a user environment that changes over time and based on environmental factors as dynamic elements, rather than static elements. Also, the accuracy of the predictions is verified through a performance evaluation between the suggested method and the previous method to validate effectiveness, and an approximate 13% performance improvement was confirmed. Currently, the acquisition of knowledge from unstructured data is in its early stages. It is expected that symbolic knowledge-acquisition technology from unstructured information that is produced and that changes in real time, and the dynamic cluster model method suggested in this study, will become the core technologies that promote the development of healthcare management technology.

Keywords

Healthcare management Dynamic cluster model CNN Knowledge discovery 

Notes

Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2018R1C1B5047242).

References

  1. 1.
    Back Ji, Chung K, Kim J, Jung H (2019) Cloud-based ontology context mining using deep learning in healthcare. IJITEE 8(2):296–300Google Scholar
  2. 2.
    Jung H, Chung K (2016) PHR based life health index mobile service using decision support model. Wirel Pers Commun 86(1):315–332CrossRefGoogle Scholar
  3. 3.
    Jung E, Kim J, Chung K, Park D (2014) Mobile healthcare application with EMR interoperability for diabetes patients. Cluster Comput 17(3):871–880CrossRefGoogle Scholar
  4. 4.
    Center for Health Industry Information & Statistics (2015) Weekly healthcare industry trends. Korea Health Industry Development Institute, no 155, pp 1–2Google Scholar
  5. 5.
    Jung H, Chung K (2016) Life style improvement mobile service for high risk chronic disease based on PHR platform. Cluster Comput 19(2):967–977CrossRefGoogle Scholar
  6. 6.
    Kim JC, Chung K (2017) Depression index service using knowledge based crowdsourcing in smart health. Wirel Pers Commun 93(1):255–268CrossRefGoogle Scholar
  7. 7.
    Jung H, Chung K (2016) Knowledge-based dietary nutrition recommendation for obesity management. Inf Technol Manag 17(1):29–42CrossRefGoogle Scholar
  8. 8.
    Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on international conference on machine learning, pp 689–696Google Scholar
  9. 9.
    Shin DH, Choi KH, Kim CB (2017) Deep learning model for prediction rate improvement of stock price using RNN and LSTM. J Korean Inst Inf Technol 15(10):9–16CrossRefGoogle Scholar
  10. 10.
    Kim JC, Chung K (2018) Neural-network based adaptive context prediction model for ambient intelligence. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-018-0972-3 Google Scholar
  11. 11.
    Chung K, Park RC (2018) Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Comput.  https://doi.org/10.1007/s10586-018-2334-5 Google Scholar
  12. 12.
    Jung H, Yang J-G, Woo J-I, Lee BM, Ouyang J, Chung K, Lee Y-H (2015) Evolutionary rule decision using similarity based associative chronic disease patients. Cluster Comput 18(1):279–291CrossRefGoogle Scholar
  13. 13.
    Jung H, Yoo H, Lee Y, Chung K-Y (2015) Interactive pain nursing intervention system for smart health service. Multimed Tools Appl 74(7):2449–2466CrossRefGoogle Scholar
  14. 14.
    An J-Y, Seo E-R, Lim K-H, Shin J-H, Kim J-B (2013) Standardization of the Korean version of screening tool for depression. J Korean Soc Biol Ther Psychiatry 19(1):47–56Google Scholar
  15. 15.
    Chung K, Yoo H, Choe DE (2018) Ambient context-based modeling for health risk assessment using deep neural network. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-018-1033-7 Google Scholar
  16. 16.
    Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Recommender systems handbook, pp 217–253Google Scholar
  17. 17.
    Chaib A, Boussebough I, Chaoui A (2018) Adaptive service composition in an ambient environment with a multi-agent system. J Ambient Intell Humaniz Comput 9(2):367–380CrossRefGoogle Scholar
  18. 18.
    Korea Centers for Disease Control and Prevention (2016) 7th Korean National Health and Nutrition Examinations Survey (KNHANES V-1). Centers for Disease Control and Prevention, Retrieved from https://knhanes.cdc.go.kr
  19. 19.
    Cho CS, An DU, Jeong SJ, Lee SW (2003) K-means clustering method according to documentation numbers. In: Proceedings of the international conference on electronics, information, and communication, pp 1557–1560Google Scholar
  20. 20.
    Kim W, Kim S (2014) Document clustering technique by K-means algorithm and PCA. J Korea Inst Inf Commun Eng 18(3):625–630CrossRefGoogle Scholar
  21. 21.
    Jung H, Chung KY, Lee YH (2015) Decision supporting method for chronic disease patients based on mining frequent pattern. Multimed Tools Appl 74(20):8979–8991CrossRefGoogle Scholar
  22. 22.
    Yoo H, Chung K (2018) Heart rate variability based stress index service model using bio-sensor. Cluster Comput 21(1):1139–1149CrossRefGoogle Scholar
  23. 23.
    Chung K, Kim JC, Park RC (2016) Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P. Inf Technol Manag 17(1):67–80CrossRefGoogle Scholar
  24. 24.
    Jung H, Yoo H, Chung K (2016) Associative context mining for ontology-driven hidden knowledge discovery. Cluster Comput 19(4):2261–2271CrossRefGoogle Scholar
  25. 25.
    Orciuoli F, Parente M (2017) An ontology-driven context-aware recommender system for indoor shopping based on cellular automata. J Ambient Intell Humaniz Comput 8(6):937–955CrossRefGoogle Scholar
  26. 26.
    Chung K, Boutaba R, Hariri S (2014) Recent trends in digital convergence information system. Wirel Pers Commun 79(4):2409–2413CrossRefGoogle Scholar
  27. 27.
    Kim JC, Chung K (2018) Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Netw Appl 11(6):1278–1287CrossRefGoogle Scholar
  28. 28.
    Kim JC, Chung K (2017) Emerging risk forecast system using associative index mining analysis. Clust Comput 20(1):547–558CrossRefGoogle Scholar
  29. 29.
    Chung K, Park RC (2017) Cloud based u-healthcare network with QoS guarantee for mobile health service. Clust Comput.  https://doi.org/10.1007/s10586-017-1120-0 Google Scholar
  30. 30.
    Song CW, Jung H, Chung K (2017) Development of a medical big-data mining process using topic modeling. Clust Comput.  https://doi.org/10.1007/s10586-017-0942-0
  31. 31.
    Yoo H, Chung K (2018) Mining-based lifecare recommendation using peer-to-peer dataset and adaptive decision feedback. Peer-to-Peer Netw Appl 11(6):1309–1320CrossRefGoogle Scholar
  32. 32.
    Baek JW, Kim JC, Chun J, Chung K (2019) Hybrid clustering based health decision-making for improving dietary habits. Technol Health Care.  https://doi.org/10.3233/THC-191730
  33. 33.
    Choi SY, Chung K (2019) Knowledge process of health big data using mapreduce-based associative mining. Pers Ubiquit Comput.  https://doi.org/10.1007/s00779-019-01230-3
  34. 34.
    Kim JC, Chung K (2019) Associative feature information extraction using text mining from health big data. Wirel Pers Commun 105(2):691–707CrossRefGoogle Scholar
  35. 35.
    Jung H, Chung K (2015) Ontology-driven slope modeling for disaster management service. Clust Comput 18(2):677–692CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Computer Science and EngineeringKyonggi UniversitySuwon-siSouth Korea
  2. 2.Department of Computer SoftwareDaelim University CollegeAnyang-siSouth Korea

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