Skip to main content

Advertisement

Log in

P2P-based open health cloud for medicine management

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Much attention has recently been given to changes in medical services, such as remote medical services and healthcare services customized for users, where cloud technology is utilized in the health and medical industries. It is possible to utilize health information needed by users in real time if medical records and health information are saved through this, so that the level of medical practice increases, and medical information in a new form can be predicted and provided based on big data analysis and processing through the accumulated medical data. Thus, studies of mobile services utilizing this are being conducted. In particular, there is an increasing demand for the development of a cloud-based service in pharmaceutical management. With the development of modern medicine, by simply taking medicine it has become possible to treat diseases that in the past might have threatened lives. When patients get prescriptions for various medicines, it is necessary to know what role and effect they have, and in order to prevent misuse and abuse, it is necessary to provide correct and accurate information about these medicines. Numerous drugs enhance quality of life, but those aimed at treatment may be lethal unless patients know how to take them. In this study, we propose a peer-to-peer (P2P)-based open health cloud for medicine management. The proposed system is designed to smoothly provide a virtual cloud service by building up various cloud environments and communicating with cloud servers in a pre-reserved, on-demand method. The aim is to resolve the problems of data processing and reduce delays with wireless body area networks (WBANs), which occur in existing cloud health services, and to enhance stability and QoS for things like response time. In addition, to integrate personal health record (PHR) data stored in an internal medical database built up by each institution, and for the integration of various medical systems, the proposed system is designed to allow access to file managers through open database connectors, and it includes source connectors that link to external systems. Based on this, an access interface was designed to provide a mobile chat-bot service for user convenience. The proposed system is a mobile health service in the form of a chat-bot to quickly deal with changes through incidents that may occur because users take the wrong medicine by mistake in their everyday lives. To build up and analyze big data based on information collected in various ways, and to provide the data to users conveniently, the service is broadly divided into five classes, and in each class and in each service class is a customized user interface/user experience.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. Korea University Anam Hospital, http://anam.kumc.or.kr/.

References

  1. Jung H, Yoo H, Chung K (2016) Associative context Mining for Ontology-Driven Hidden Knowledge Discovery. Clust Comput 19(4):2261–2271

    Article  Google Scholar 

  2. Yoo H, Chung K (2017) PHR based diabetes index service model using life behavior analysis. Wirel Pers Commun 93(1):161–174

    Article  Google Scholar 

  3. Farahani B, Barzegari M (2019) "Towards collaborative machine learning driven Healthcare internet of things", In Proc of the International Conference on Omni-Layer Intelligent Systems pp. 134–140

  4. Solis R, Pakbin A, Akbari A, Mortazavi BJ, Jafari R (2019) "A human-centered wearable sensing platform with intelligent automated data annotation capabilities", In Proc. of international conference on internet of things design and implementation. pp. 255–260

  5. Chung CF, Wang Q, Schroeder J, Cole A, Zia J, Fogarty J, Munson SA (2019) Identifying and planning for individualized change: patient-provider collaboration using lightweight food diaries in healthy eating and irritable bowel Syndromenet of things design and implementation. Proc ACM Interactive Mob Wearable Ubiquit Technol 3:1–27

    Article  Google Scholar 

  6. Chung K, Park RC (2016) PHR open platform based smart health service using distributed object group framework. Clust Comput 19(1):505–517

    Article  Google Scholar 

  7. Luo Y, Jin H, Li P (2019) "A Blockchain future for secure clinical data sharing: a position paper", In Proc of ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization pp. 23–27

  8. Raj S, Lee JM, Garrity A, Newman MW (2019) "Clinical data in context: towards Sensemaking tools for interpreting personal health data". Proc ACM Interactive Mob Wearable Ubiquit Technol 3(1)

    Article  Google Scholar 

  9. Kim JC, Chung K (2017) Emerging risk forecast system using associative index mining analysis. Clust Comput 20(1):547–558

    Article  Google Scholar 

  10. Kim JC, Chung K (2017) Depression index service using knowledge based crowdsourcing in smart health. Wirel Pers Commun 93(1):255–268

    Article  Google Scholar 

  11. Yoo H, Chung K (2018) Heart rate variability based stress index service model using bio-sensor. Clust Comput 21(1):1139–1149

    Article  Google Scholar 

  12. Chung K, Park RC (2019) Cloud based u-healthcare network with QoS guarantee for Mobile health service. Clust Comput. 22(Suppl 1):2001–2015. https://doi.org/10.1007/s10586-017-1120-0

    Article  Google Scholar 

  13. Chung K, Kim JC, Park RC (2016) Knowledge-based health service considering user convenience using hybrid Wi-fi P2P. Inf Technol Manag 17(1):67–80

    Article  Google Scholar 

  14. Monteiro K, Rocha É, Silva É, Santos GL, Santos W, Endo PT 2018 "Developing an e-health system based on IoT, fog and cloud computing", IEEE/ACM international conference on utility and cloud computing companion (UCC companion). https://doi.org/10.1109/UCC-Companion.2018.00024

  15. Tawalbeh LA, Tawalbeh H, Song H, Jararweh Y (2017) "Intrusion and attacks over mobile networks and cloud health systems", IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). https://doi.org/10.1109/INFCOMW.2017.8116345

  16. Khan P, Ullah N, Ullah S, Kwak KS (2011) Seamless interworking architecture for WBAN in heterogeneous wireless networks with QoS guarantees. J Med Syst 35(5):1313–1321

    Article  Google Scholar 

  17. Jung EY, Kim JH, Chung KY, Park DK (2013) Home health gateway based Healthcare services through U-health platform. Wirel Pers Commun 73(2):207–218

    Article  Google Scholar 

  18. Chung K, Park RC (2016) P2P cloud network services for IoT based disaster situations information. Peer-to-Peer Netw Appl 9(3):566–577

    Article  Google Scholar 

  19. Jung H, Chung K (2015) Sequential pattern profiling based bio-detection for smart health service. Clust Comput 18(1):209–219

    Article  Google Scholar 

  20. Liao LD, Wang IJ, Chang CJ, Lin BS (2010) "Human cognitive application by using wearable Mobile brain computer Interface", In Proc IEEE Region 10 conference TENCON 2010, Vol. 1, pp. 346–351

  21. Park RC, Jung H, Chung K, Yoon KH (2015) Picocell based telemedicine health Service for Human UX/UI. Multimed Tools Appl 74(7):2519–2534

    Article  Google Scholar 

  22. Diasend Healthcare. https://www.diasend.com/en/. Accessed 5 April 2019

  23. OMOP-CDM of odyssey consortium. http://www.ohdsi.org/. Accessed 17 March 2019

  24. Philips HealthSuite. https://www.usa.philips.com/healthcare/innovation/about-health-suite/. Accessed 5 March 2019

  25. PaasTa Cloud Platform. https://www.paas-ta.kr/. Accessed 30 March 2019

  26. Jung H, Chung K (2016) Knowledge-based dietary nutrition recommendation for obese management. Inf Technol Manag 17(1):29–42

    Article  Google Scholar 

  27. Kim J, Chung K (2018) Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Netw Appl 11(6):1278–1287

    Article  Google Scholar 

  28. Akter M, Gani A, Rahman MO, Hassan MM, Almogren A (2018) Performance analysis of personal cloud storage Services for Mobile Multimedia Health Record Management. IEEE Access 6:52625–52638

    Article  Google Scholar 

  29. Chung K, Yoo H, Choe D, Jung H (2019) Blockchain network based topic mining process for cognitive manufacturing. Wirel Pers Commun 105(2):583–597

    Article  Google Scholar 

  30. 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):1309–1320

    Article  Google Scholar 

  31. Kim KW, Kim TM, Lee CH, Kim JE, Hong JH (2017) "Image recognition and conversation based medical products information chat-bot service". Proc Inst Signal Process Syst 13–17

  32. Kim JC, Chung K (2019) Associative feature information extraction using text mining from health big data. Wirel Pers Commun 105(2):691–707

    Article  MathSciNet  Google Scholar 

  33. Chung K, Boutaba R, Hariri S (2014) Recent trends in digital convergence information system. Wirel Pers Commun. 79(4):2409–2413

    Article  Google Scholar 

  34. Song CW, Jung H, Chung K (2019) Development of a medical big-data mining process using topic modeling. Clust Comput. 22(Suppl 1):1949–1958. https://doi.org/10.1007/s10586-017-0942-0

    Article  Google Scholar 

  35. Baek JW, Kim JC, Chun J, Chung K (2019) Hybrid clustering based health decision-making for improving dietary habits. Technology and Health Care. https://doi.org/10.3233/THC-191730

    Article  Google Scholar 

  36. Choi SY, Chung K (2019) Knowledge process of health big data using mapreduce-based associative mining. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-019-01230-3

  37. Park SS, Chung K (2019) MMCNet: Deep Learning-based Multimodal Classification Model using Dynamic Knowledge. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-019-01261-w

  38. Kim JC, Chung K (2019) Discovery of Knowledge of Associative Relations using Opinion Mining Based on a Health Platform. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-019-01231-2

  39. Jung H, Chung K (2015) Ontology-driven Slope Modeling for Disaster Management Service. Clust Comput 18(2):677–692

    Article  Google Scholar 

  40. Kim JC, Chung K (2019) Prediction Model of User Physical Activity using Data Characteristicsbased Long Short-term Memory Recurrent Neural Networks. KSII Transactions on Internet and Information Systems 13(4):2060–2077

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2019R1F1A1060328).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roy C. Park.

Additional information

This article is part of the Topical Collection: Special Issue on P2P Computing for Intelligence of Things

Guest Editors: Sunmoon Jo, Jieun Lee, Jungsoo Han, and Supratip Ghose

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chung, K., Park, R.C. P2P-based open health cloud for medicine management. Peer-to-Peer Netw. Appl. 13, 610–622 (2020). https://doi.org/10.1007/s12083-019-00791-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-019-00791-7

Keywords

Navigation