Advancing clinical research by semantically interconnecting aggregated medical data information in a secure context

  • Athos Antoniades
  • Aristos Aristodimou
  • Christos Georgousopoulos
  • Nikolaus Forgó
  • Ann Gledson
  • Panagiotis Hasapis
  • Caroline Vandeleur
  • Konstantinos Perakis
  • Ratnesh Sahay
  • Muntazir Mehdi
  • Christiana A. Demetriou
  • Marie-Pierre F. Strippoli
  • Vasiliki Giotaki
  • Myrto Ioannidi
  • David Tian
  • Federica Tozzi
  • John Keane
  • Constantinos Pattichis
Original Paper
Part of the following topical collections:
  1. Systems Medicine

Abstract

Electronic Health Records (EHRs) contain an increasing wealth of medical information. When combined with molecular level data, they enhance the understanding of the underlying biological mechanisms of diseases, enabling the identification of key prognostic biomarkers to disease and treatment outcomes. However, the European healthcare information space is fragmented due to the lack of legal and technical standards, cost effective platforms, and sustainable business models. There is a clear need for a framework facilitating the efficient and homogenized access to anonymized distributed EHRs, merged from multiple data sources into a single data analysis space. In this paper we present the outcomes of Linked2Safety, a project that proposes a solution to these problems by providing a semantically interconnected approach to sharing aggregate data in the form of data cubes. This approach eliminates the risks associated with sharing pseudoanonymized (and therefore still personal) data while enabling the multi-source, multi-type analysis of health data through a single web based secure access platform. The Linked2Safety system is evaluated by external to the project Medical science analysts, Analytic methodology engineers and Data providers with respect to five specific dimensions of the system (analysis space, linked data space, usability of the system, legal and ethical issues, and value of the system) in this paper. For all five dimensions that were examined, the participants’ perceptions were overwhelmingly positive.

Keywords

Semantic Interoperability Linked2Safety Electronic health records Personal data protection Anonymity Adverse Event prediction Genetic analysis 

References

  1. 1.
    C. Martínez-Costa, D. Kalra, and S. Schulz, “Improving EHR semantic interoperability: future vision and challenges,” Stud Health Technol Inform. 2014;205:589–93.Google Scholar
  2. 2.
    Kalra D, Schmidt A, Potts HWW, Dupont D, Sundgren M, De Moor G, EHR4CR Research Consortium (2011) Case report from the EHR4CR project—a European survey on electronic health records systems for clinical research. iHealth Connect, 1 (2) 108–113.Google Scholar
  3. 3.
    Chniti A, Traore L, Hussain S, Griffon N. Stéfan Jacques Darmoni, Jean Charlet, Eric Sadou, David Ouagne, Eric Lepage, Christel Daniel: a semantic interoperability framework for facilitating cross-hospital exchanges. MIE, 2014, vol. 205, p. 1255.Google Scholar
  4. 4.
    Gokce B. Laleci, Mustafa Yuksel, Asuman Dogac, Providing Semantic Interoperability between Clinical Care and Clinical Research Domains, IEEE Trans Inf Technol Biomed, Volume: 17, Issue: 2, March 2013 (online since Sept. 2012), Page(s): 356–369.Google Scholar
  5. 5.
    Gokce B. Laleci Erturkmen, Asuman Dogac, Mustafa Yuksel, Sajjad Hussain, Gunnar Declerck, Christel Daniel, Hong Sun, Kristof Depraetere, Dirk Colaert, Jos Devlies, Tobias Krahn, Bharat Thakrar, Gerard Freriks, Tomas Bergvall, Ali Anil Sinaci, Building the Semantic Interoperability Architecture Enabling Sustainable Proactive Post Market Safety Studies, Accepted as a poster in SIMI 2012 Wokshop (Semantic Interoperability in Medical Informatics), in ESCW 2012: Extended Semantic Web Conference, May 27, 2012 in Heraklion (Crete), Greece (Poster).Google Scholar
  6. 6.
    R. Sahay, W. Akhtar, and R. Fox, “PPEPR: Plug and Play Electronic Patient Records,” in Proceedings of the 2008 ACM Symposium on Applied Computing, NY, USA, 2008, pp. 2298–2304.Google Scholar
  7. 7.
    Directive E. 95/46/ec of the european parliament and of the council of 24 october 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Off J Eur Communities. 1995;281:31–50.Google Scholar
  8. 8.
    Faden R, Beauchamp T, King N. A history and theory of informed consent. USA: Oxford University Press; 1986.Google Scholar
  9. 9.
    “Privireal: Data protection - greece,” Aug 2012. [Online]. Available:http://www.privireal.org/content/dp/greece.php
  10. 10.
    “Office of the commissioner for personal data protection - home page,” Aug 2012. [Online]. Available: http://www.dataprotection.gov.cy/dataprotection/dataprotection.nsf/d1813 d5911e138bdc2256cbd00313d1c/f8e24ef90a27f34f c2256eb4002854e7
  11. 11.
    “Federal act on data protection,” Aug 2012. [Online]. Available:http://www.vud.ch/generaldocs/vud revdsg/235.1 FADP en.pdf
  12. 12.
    Firmann M, et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord. Mar. 2008;8:6.CrossRefGoogle Scholar
  13. 13.
    Preisig M, et al. The PsyCoLaus study: methodology and characteristics of the sample of a population-based survey on psychiatric disorders and their association with genetic and cardiovascular risk factors. BMC Psychiatry. 2009;9:9.CrossRefGoogle Scholar
  14. 14.
    A. Antoniades et al., “The effects of applying cell-suppression and perturbation to aggregated genetic data,” in IEEE 12th International Conference on Bioinformatics and Bioengineering (BIBE), Larnaka, Cyprus, 2012, pp. 644–649.Google Scholar
  15. 15.
    A.-C. N. Ngomo and S. Auer, “LIMES: A Time-efficient Approach for Large-scale Link Discovery on the Web of Data,” in Proceedings of the 22nd IJCAI, Barcelona, Catalonia, Spain, 2011, vol. 3, pp. 2312–2317.Google Scholar
  16. 16.
    Khan Y, Saleem M, Mehdi M, Hogan A, Mehmood Q, Rebholz-Schuhmann D, Sahay R. SAFE: SPARQL Federation over RDF Data Cubes with Access Control. J Biomed Semantics. 2017;8(1):5.CrossRefGoogle Scholar
  17. 17.
    R. Sahay, D. Ntalaperas, E. Kamateri, P. Hasapis, O. D. Beyan, M. F. Strippoli, C. Demetriou, T. Gklarou-Stavropoulou, M. Brochhausen, K. A. Tarabanis, T. Bouras, D. Tian, A. Aristodimou, A. Antoniades, C. Georgousopoulos, M. Hauswirth, and S. Decker. “An ontology for clinical trial data integration", in 2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013, pp. 3244–3250.Google Scholar
  18. 18.
    E. Kamateri, E. Kalampokis, E. Tambouris, and K. Tarabanis, “The linked medical data access control framework,” J Biomed Inform, vol. 50, pp. 213–225, Aug. 2014.Google Scholar

Copyright information

© IUPESM and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Athos Antoniades
    • 1
    • 2
  • Aristos Aristodimou
    • 1
    • 2
  • Christos Georgousopoulos
    • 3
  • Nikolaus Forgó
    • 4
  • Ann Gledson
    • 5
  • Panagiotis Hasapis
    • 3
  • Caroline Vandeleur
    • 6
  • Konstantinos Perakis
    • 7
  • Ratnesh Sahay
    • 8
  • Muntazir Mehdi
    • 8
  • Christiana A. Demetriou
    • 9
  • Marie-Pierre F. Strippoli
    • 6
  • Vasiliki Giotaki
    • 10
  • Myrto Ioannidi
    • 10
  • David Tian
    • 11
  • Federica Tozzi
    • 1
    • 2
  • John Keane
    • 5
  • Constantinos Pattichis
    • 1
  1. 1.University of CyprusNicosiaCyprus
  2. 2.Stremble Ventures LTDLimassolCyprus
  3. 3.INTRASOFT InternationalLuxembourg CityLuxembourg
  4. 4.Gottfried Wilhelm Leibniz Universität HannoverHannoverGermany
  5. 5.University of ManchesterManchesterUK
  6. 6.Centre Hospitalier Universitaire VaudoisLausanneSwitzerland
  7. 7.Ubitech Ltd.AthensGreece
  8. 8.Insight Centre for Data AnalyticsNUI GalwayGalwayIreland
  9. 9.Cyprus Institute of Neurology & GeneticsNicosiaCyprus
  10. 10.Zeincro Hellas S.A.VrillisiaGreece
  11. 11.Leeds Beckett UniversityLeedsUK

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