Advertisement

Improving the Use, Analysis and Integration of Patient Health Data

  • David Hansen
  • Mohan Karunanithi
  • Michael Lawley
  • Anthony Maeder
  • Simon McBride
  • Gary Morgan
  • Chaoyi Pang
  • Olivier Salvado
  • Antti Sarela
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4977)

Abstract

Health Information Technologies (HIT) are being deployed world- wide to improve access to individual patient information. Primarily this is through the development of electronic health records (EHR) and electronic medical records (EMR). While the proper collection of this data has reached a high level of maturity, the use and analysis of it is only in its infancy. This data contains information which can potentially improve treatment for the individual patient and for the cohort of patients suffering a similar disease. The data can also provide valuable information for broader research purposes. In this paper we discuss the research contributions we are making in improving the use and analysis of patient data. Our projects include the analysis of physiological data, the extraction of information from multi-modal data types, the linking of data stored in heterogeneous data sources and the semantic integration of data. Through these projects we are providing new ways of using health data to improve health care delivery and provide support for medical research.

Keywords

ambulatory monitoring cohort analysis data linking electronic health records semantic integration time series analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Lutz, C., Suntisrivaraporn, B.: CEL - a polynomial-time reasoner for life science ontologies. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 287–291. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Boyle, J., Bidargaddi, N., Sarela, A., Karunanithi, M.: Ambulatory care delivery for chronic disease management. In: Proceedings of the 6th IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, pp. 384–389 (2008)Google Scholar
  3. 3.
    Churches, T., Christen, P.: Some methods for blindfolded record linkage. BMC Medical Informatics and Decision Making 4(9), 17 (2004)Google Scholar
  4. 4.
    Hansen, D.P., Pang, C., Maeder, A.: HDI: integrating health data and tools. Journal of Soft Computing 11(4), 361–367 (2007)CrossRefGoogle Scholar
  5. 5.
    Lawley, M.: Exploiting fast classification of SNOMED CT for query and integration of health data. In: Proceedings of KR-MED 2008, Phoenix, USA (to appear, 2008)Google Scholar
  6. 6.
    Nunez, C.: Advanced techniques for anesthesia data analysis. Seminars in Anesthesia, Perioperative Medicine and Pain 23(2), 121–124 (2004)CrossRefGoogle Scholar
  7. 7.
    McCowan, I.A., Moore, D.C., Nguyen, A.N., Bowman, R.V., Clarke, B.E., Duhig, E.E., Fry, M.J.: Collection of cancer stage data by classifying free-text medical reports. Journal of the American Medical Informatics Association 14(6), 736–745 (2007)CrossRefGoogle Scholar
  8. 8.
    O’Keefe, C.M., Yung, M., Baxter, R.: Privacy-preserving linkage and data extraction protocol. In: Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society, Washington, DC, USA, pp. 94–102 (2004)Google Scholar
  9. 9.
    Pang, C., Zhang, Q., Hansen, D., Maeder, A.: Building data synopses within a known maximum error bound. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM 2007. LNCS, vol. 4505, pp. 463–470. Springer, Berlin (2007)CrossRefGoogle Scholar
  10. 10.
    Pare, G., Jaana, M., Sicotte, C.: Systematic review of home telemonitoring for chronic diseases. Journal of the American Medical Informatics Association 14(3), 269–277 (2007)CrossRefGoogle Scholar
  11. 11.
    Sittig, D.F.: Grand challenges in medical informatics. Journal of the American Medical Informatics Association 1(5), 412–413 (1994)CrossRefGoogle Scholar
  12. 12.
    Wache, H., Vogele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hubner, S.: Ontology-based integration of information - a survey of existing approaches. In: Proceedings of the Workshop on Ontologies and Information Sharing, Seattle, USA, pp. 108–117 (2001)Google Scholar
  13. 13.
    Wade, G., Rosenbloom, S.T.: Experiences mapping a legacy interface terminology to SNOMED CT. In: Proceedings of the Semantic Mining Conference on SNOMED CT, Copenhagen, Denmark, p. 5 (2006)Google Scholar
  14. 14.
    Zuluaga, M., Acosta, O., Bourgeat, P., Salvado, O., Hernandez, M., Ourselin, S.: Cortical thickness measurement from magnetic resonance images using partial volume estimation. In: Reinhardt, J.M., Pluim, J.P.W. (eds.) Proceedings of SPIE: Image Processing, vol. 6914, 69140J1–69140J8 (to appear, 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David Hansen
    • 1
  • Mohan Karunanithi
    • 1
  • Michael Lawley
    • 1
  • Anthony Maeder
    • 1
  • Simon McBride
    • 1
  • Gary Morgan
    • 1
  • Chaoyi Pang
    • 1
  • Olivier Salvado
    • 1
  • Antti Sarela
    • 1
  1. 1.The Australian e-Health Research Centre, CSIRO ICT CentreBrisbaneAustralia

Personalised recommendations