Abstract
Current devices and sensors have revolutionized our daily lives, with the healthcare domain exploring and adapting new technologies. The rapid explosion of digital healthcare happened with the help of current 4G LTE technologies including innovations such as the continuous monitoring of patient vitals, teleporting doctors to a virtual environment or leveraging Artificial Intelligence to generate new medical insights. The arised problem is that current 4G LTE based communication platforms will not be able to keep up with the exploding connectivity demands. This is where the new 5G technology comes, expected to support ultra-reliable, low-latency and massive data communications. In this paper, an end-to-end approach is being provided in the healthcare domain for gathering medical data, anonymizing it, cleaning it, making it interoperable, and finally storing it through 5G network technologies, for their transmission to a different location, supporting real-time results and decision-making.
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References
Population health outcomes. http://www.healthcatalyst.com/population-health-outcomes-3-keys-to-drive-improvement
The role of IoT in the healthcare industry. https://hackernoon.com/the-role-of-internet-of-things-in-the-healthcare-industry-759b2a1abe5
Healthcare needs 5G. https://www.chilmarkresearch.com/healthcare-needs-5g/
How will 5G impact different industries? http://prescouter.com/2018/01/5g-impact-different-industries
The Journey to 5G. http://www.healthcareitnews.com/news/journey-5g
Pires, F., et al.: A platform for integrating physical devices in the Internet of Things. In: Embedded and Ubiquitous Computing (EUC), pp. 234–241. IEEE (2014)
Gong, P.: Dynamic integration of biological data sources using the data concierge. Health Inf. Sci. Syst. 1, 1–19 (2013)
GDPR requirements. https://www.delphix.com/white-paper/gdpr
El Emam, K., Arbuckle, L.: Anonymizing Health Data: Case Studies and Methods to get you started, 2nd edn, p. 1005. O’Reilly Media Inc., Newton (2013)
Kruger, P., Hancke, G.: Benchmarking internet of things data sources. In: 12th IEEE International Conference on Industrial Informatics (INDIN). IEEE (2014)
Macfarlane, S., Tannath, T., Scott, J., Kelly, V.: The validity and reliability of global positioning systems in team sport: a brief review. JSCR 30(5), 1470–1490 (2016)
Mead, C.: Data interchange standards in healthcare IT-computable semantic interoperability. JHIM 20, 71–78 (2006)
HL7 FHIR. https://www.hl7.org/fhir/
HEALTHCARE 4.0: A NEW WAY OF LIFE? http://www.vph-institute.org/news/healthcare-4-0-a-new-way-of-life.html
A new Generation of eHealth Systems Powered by 5G. http://www.wwrf.ch/files/wwrf/content/files/publications/outlook/Outlook17.pdf
5G on eHealth. https://5g-ppp.eu/wp-content/uploads/2016/02/5G-PPP-White-Paper-on-eHealth-Vertical-Sector.pdf
INTERNET OF THINGS & 5G REVOLUTION. http://www.astrid-online.it/static/upload/stud/studio-i-com_internet_5g_.pdf
Mishra, A., Agrawal, P.: Continuous health condition monitoring by 24 × 7 sensing and transmission of physiological data over 5G cellular channels. In: ICNC, pp. 584–590 (2015)
Banaee, H., et al.: Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12), 17472–17500 (2013)
Ryan, M., et al.: Facilitating health behaviour change and its maintenance: interventions based on self-determination theory. Eur. Health Psychol. 10, 2–5 (2008)
Oleshchuk, V., Fensli, R.: Remote patient monitoring within a future 5G infrastructure. Wirel. Pers. Commun. 57, 431–439 (2011)
Mattos, W., Gondim, P.: M-health solutions using 5G networks and M2M communications. IT Prof. 18(3), 24–29 (2016)
Leventer-Roberts, M., Balicer, R.: Data integration in health care. In: Amelung, V., Stein, V., Goodwin, N., Balicer, R., Nolte, E., Suter, E. (eds.) Handbook Integrated Care, pp. 121–129. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56103-5_8
Rolim, C.O., et al.: A cloud computing solution for patient’s data collection in health care institutions. In: Second International Conference on ETELEMED 2010. IEEE (2010)
Carbonaro, A., Piccinini, F., Reda, R.: Integrating heterogeneous data of healthcare devices to enable domain data management. JeLKS 14(1), 45–56 (2018)
Pötter, B., Sztajnberg, A.: Adapting heterogeneous devices into an IoT context-aware infrastructure. In: Software Engineering for Adaptive and Self-Managing, pp. 64–74. ACM (2016)
Globle, C., et al.: Transparent access to multiple bioinformatics information sources. IBM Syst. J. 40, 534–551 (2001)
Donelson, L., et al.: The BioMediator system as a data integration tool to answer diverse biologic queries. In: Proceedings of MedInfo, pp. 768–772 (2004)
Philippi, S.: Light-weight integration of molecular biological databases. Bioinformatics 20, 51–57 (2004)
Eckman, B., Lacroix, Z., Raschid, L.: Optimized seamless integration of biomolecular data. In: IEEE International Conference on Bioinformatics and Biomedical Engineering, pp. 23–32 (2001)
Martín, L., et al.: Ontology based integration of distributed and heterogeneous data sources in ACGT. In: HEALTHINF, pp. 301–306 (2008)
Jabbar, S., et al.: Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel. Commun. Mobile Comput. (2017)
Truta, T., Vina, B.: Privacy protection: p-sensitive k-anonymity property. In: 22nd International Conference on Data Engineering Workshops, Atlanta (2006)
El Emam, K.: Data anonymization practices in clinical research. a descriptive study. University of Ottawa (2006)
El Emam, K., et al.: A systematic review of re-identification attacks on health data. PLoS One 6(12), e28071 (2011)
Zhong, S., et al.: Privacy-enhancing k-anonymization of customer data. In: PODS 2005, pp. 139–147 (2004)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Unc. Fuzz. Knowl. Based Syst. 10(5), 557–570 (2002)
Benjamin, E., et al.: Systematic literature review on the anonymization of high dimensional streaming datasets for health data sharing. Procedia Comput. Sci. 63, 348–355 (2015)
Dubovitskaya, A., Urovi, V., Vasirani, M., Aberer, K., Schumacher, M.I.: A cloud-based eHealth architecture for privacy preserving data integration. In: Federrath, H., Gollmann, D. (eds.) SEC 2015. IAICT, vol. 455, pp. 585–598. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18467-8_39
Li, H., et al.: (a, k)-anonymous scheme for privacy-preserving data collection in IoT-based healthcare services systems. J. Med. Syst. 42(3), 56 (2018)
Lu, Y., Sinnott, R.O., Verspoor, K.: A semantic-based k-anonymity scheme for health record linkage. Stud. Health Technol. Inform. 239, 84–90 (2017)
Lu, Y., Verspoor, K., Sinnott, R.O., Parampalli, U.: Effective preservation of privacy during record linkage. In: School of Computing and Information Systems, p. 25 (2017)
Fatima, A., Nazir, N., Gufran, K.: Data cleaning in data warehouse: a survey of data pre-processing techniques and tools. JITCS 9, 50–61 (2017)
Rahm, E., Do, H.: Data cleaning: problems and current approaches. IEEE Bull. Tech. Comm. Data Eng. 23(4), 2000–2012 (2000)
Krishnan, S., Haas, D., Franklin, M., Wu, E.: Towards reliable interactive data cleaning: a user survey and recommendations. In: HILDA, California (2016)
Dallachiesa, M., et al.: NADEEF: a commodity data cleaning system. In: ACM SIGMOD International Conference on Management of Data, New York (2013)
Dagade, A., Mali, M., Pathak, N.: Survey of data duplication detection and elimination in domain dependent and domain-independent databases. IJARCSMS 4(5), 238–243 (2016)
Benjelloun, O., et al.: Swoosh: A Generic Approach to Entity Resolution. Stanford InfoLab, Stanford (2005)
Bohannon, P., Fan, W., Flaster, M., Rastogi, R.: A cost-based model and effective heuristic for repairing constraints by value modification. In: ACM SIGMOD (2005)
Cong, G., Fan, W., Geerts, G., Jia, X., Ma, S.: Improving data quality: consistency and accuracy. In: The 33rd International Conference on Very Large Data Bases, Vienna (2007)
Fan, W., et al.: Towards certain fixes with editing rules and master data. VLDB J. 21(2), 213–238 (2012)
Yakout, M., et al.: Guided data repair. Proc. VLDB Endowment 4(5), 279–289 (2011)
Cheng, K., Hong, J.: A novel data cleaning with data matching. Adv. Sci. Technol. Lett. 136, 161–169 (2016)
Gohel, A., et al.: A commodity data cleaning system. Int. Res. J. Eng. Technol. 4(5), 1011–1014 (2017)
Joseph, W.: Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J. Strength Cond. Res. 19(1), 231 (2005)
Toporkov, A.: Criteria and methods for assessing reliability of medical equipment. Biomed. Eng. 42(1), 11–16 (2008)
Mudasir, A.: Reliability models for the internet of things: a paradigm shift. In: IEEE International Symposium on ISSREW. IEEE (2014)
Zin, T.T., et al.: Reliability and availability measures for Internet of Things consumer world perspectives. In: 5th Global Conference on Consumer Electronics. IEEE (2016)
Ryan, R., et al.: Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment. J. Sci. Med. Sport 20(6), 578–582 (2017)
Kooiman, T., et al.: Reliability and validity of ten consumer activity trackers. BMC Sport. Sci. Med. Rehabil. 7(1), 24 (2015)
Ruggiero, L., et al.: Validity and reliability of two field-based leg stiffness devices: implications for practical use. J. Appl. Biomech. 32(4), 415–419 (2016)
Justin, L., et al.: Reliability and validity of a point-of-care sural nerve conduction device for identification of diabetic neuropathy. PLoS One 9(1), e86515 (2014)
Misra, P., et al.: An interoperable realization of smart cities with plug and play based device management (2015)
Rastegar-Mojarad, M., et al.: Need of informatics in designing interoperable clinical registries. Int. J. Med. Inform. 108, 78–84 (2017)
Introduction to HL7 Standards. http://www.hl7.org/implement/standards/
The HL7 Clinical Document Architecture. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC130066/
Goossen, W., et al.: Detailed clinical models. Healthc. Inform. 16, 201–214 (2010)
Wardle, M., Spencer, A.: Implementation of SNOMED CT in an online clinical database. Futur. Hosp. J. 4(2), 126–130 (2017)
EURECA EU project. https://www.dceureca.eu/
Dogac, A., et al.: Artemis: deploying semantically enriched web services in the healthcare domain. Inf. Syst. 31, 321–339 (2006)
Schulz, S., Udo, H.: Part-whole representation and reasoning in formal biomedical ontologies. AI Med. 34(3), 179–200 (2005)
Ryan, A., Eklund, P.: A framework for semantic interoperability in healthcare. Stud. Health Tech Inform. 136, 759 (2008)
Marsch, P., et al.: 5G radio access network architecture: design guidelines and key considerations. IEEE Commun. Mag. 54(11), 24–32 (2016)
VNF. https://searchsdn.techtarget.com/definition/virtual-network-functions
Ferreira, L., et al.: An architecture to offer cloud-based radio access network as a service. In: European Conference on Networks and Communications. IEEE (2014)
Network Functions Virtualisation. http://www.etsi.org/technologies-clusters/technologies/nfv
5G Development and Validation Platform for global Industry-specific Network Services and Apps. http://5gtango.eu/
Parada, C., et al.: 5GTANGO: A Beyond-MANO Service Platform (in press)
Open Source MANO. http://www.etsi.org/technologies-clusters/technologies/nfv/open-source-mano
Mavrogiorgou, A., Kiourtis, A., Kyriazis, D.: Plug‘n’play IoT devices: an approach for dynamic data acquisition from unknown heterogeneous devices. In: Barolli, L., Terzo, O. (eds.) CISIS 2017. AISC, vol. 611, pp. 885–895. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61566-0_84
Mavrogiorgou, A., Kiourtis, A., Kyriazis, D.: A comparative study of classification techniques for managing IoT devices of common specifications. In: Pham, C., Altmann, J., Bañares, J.Á. (eds.) GECON 2017. LNCS, vol. 10537, pp. 67–77. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68066-8_6
Kiourtis, A., et al.: Aggregating heterogeneous health data through an ontological common health language. In: DeSE 10th International Conference. IEEE (2017)
Acknowledgements
A. Mavrogiorgou and A. Kiourtis would like to acknowledge the financial support from the “Hellenic Foundation for Research & Innovations (HFRI)”. Moreover, part of this work has been partially supported by the 5GTANGO project, funded by the European Commission under Grant number H2020ICT-2016-2 761493 through the Horizon 2020 and 5G-PPP programs (http://5gtango.eu).
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Mavrogiorgou, A., Kiourtis, A., Touloupou, M., Kapassa, E., Kyriazis, D., Themistocleous, M. (2019). The Road to the Future of Healthcare: Transmitting Interoperable Healthcare Data Through a 5G Based Communication Platform. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_30
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