Intensive Care Unit Platform for Health Care Quality and Intelligent Systems Support

  • M. Campos
  • A. Morales
  • J. M. Juárez
  • J. Sarlort
  • J. Palma
  • R. Marín
Part of the Advances in Soft Computing book series (AINSC, volume 50)


The underlying idea in this work consists on providing added values utilities that allow exploiting the Electronic Health Record (EHR) as something more than a simple information record. The key for providing added value to the clinical information systems is to exploit the synergy “Information + intelligence + ubiquity”. Based on this idea, we propose a distributed architecture that deals with: 1) Database and an integration layer to exploit the data stored and its integration with external information system, 2) Tools for support the medical knowledge management, 3) Tools for supervision and analysis of the health care quality (based on EBM and Clinical Guidelines) 4) Intelligent Assistance Tools.


Distributed Clinical Information System CH4 knowledge management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Campos, M., Juárez, J.M., Palma, J.T., Marín, R.: Temporal data mining with temporal constraints. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 67–76. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Chen, J., He, H., Jin, H., McAullay, D., Williams, G., Kelman, C.: Identifying risk groups associated with colorectal cancer. In: Williams, G.J., Simoff, S.J. (eds.) Data Mining. LNCS (LNAI), vol. 3755, pp. 260–272. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    DeClercq, P.A., Blom, J.A., Korsten, H.H.M., Pasman, A.: Approaches for creating computer interpretable guidelines that facilitate decision support. Artificial Intelligence in Medicine 31, 1–27 (2004)CrossRefGoogle Scholar
  4. 4.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview. In: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)Google Scholar
  5. 5.
    Field, M.J., Lohr, K.N.(eds.): Guidelines for clinical practice: from development to use. National Academy Press, Washington (1992)Google Scholar
  6. 6.
    Gunter, T.D., Terry, N.P.: The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. J. Med. Internet Res. 7(1), 3 (2005)CrossRefGoogle Scholar
  7. 7.
    Guil, F., Juárez, J.M., Marín, R.: Mining Possibilistic Temporal Constraint Networks: A Case Study in Diagnostic Evolution at Intensive Care Units. In: Proc. of the Intelligent Data Analysis in Biomedicine and Pharmacology, IDAMAP 2006, Verona, Italy, pp. 7–12 (2006)Google Scholar
  8. 8.
    Hung, S.Y., Chen, C.Y.: Mammographic case base applied for supporting image diagnosis of breast lesion. Expert Systems with Applications 30(1), 93–108 (2006)CrossRefGoogle Scholar
  9. 9.
    Juarez, J.M., Guil, F., Palma, J., Marin, R.: An uncertain temporal similarity proposal for temporal CBR. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 210–219. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Knaup, P., Wiedemann, T., Bachert, A., Creutzig, U., Haux, R., Schilling, F.: Efficiency and safety of chemotherapy plans for children: Catipoa nation wide approach. Artificial Intelligence in Medicine (24), 229–242 (2002)Google Scholar
  11. 11.
    Montani, S., Terenziani, P., Bottrighil, A.: Exploiting decision theory for supporting therapy selection in computerized clinical guidelines. In: Proc. of the 10th Conference on Artificial Intelligence in Medicine, pp. 136–140 (2005)Google Scholar
  12. 12.
    Powell, J., Buchan, I.: Electronic Health Records Should Support Clinical Research. J. Med. Internet Res. 7(1), 4 (2005)CrossRefGoogle Scholar
  13. 13.
    Sackett, D.L., Rosenberg, W.M., Gray, J.A., Haynes, R.B., Richardson, W.S.: Evidence based medicine: what it is and what it isn’t. BMJ (British Medical Journal) 312(7023), 71–72 (1996)Google Scholar
  14. 14.
    Shortliffe, E.H.: MYCIN: rule-based computer program for advising physicians regarding antimicrobial therapy selection. Ph.D. thesis, Stanford University (1974)Google Scholar
  15. 15.
    Terenziani, P., Montani, S., Bottrighi, A., Molino, G., Torchio, M.: Clinical guidelines adaptation: managing authoring and versioning issues. In: Proc. of the 10th Conference on Artificial Intelligence in Medicine, pp. 151–155 (2005)Google Scholar
  16. 16.
    Toma, T., Abu-Hanna, A., Bosman, R.: Predicting mortality in the intensive care unit using episodes. In: Proc. of the 1st International Work-conference on the Interplay between Natural and Artificial Computation, pp. 447–458 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. Campos
    • 1
  • A. Morales
    • 2
  • J. M. Juárez
    • 2
  • J. Sarlort
    • 2
  • J. Palma
    • 2
  • R. Marín
    • 2
  1. 1.Departamento de Informática y SistemasUniversidad de MurciaMurciaSpain
  2. 2.Departamento de Ingeniería de la Información y las ComunicacionesUniversidad de MurciaMurciaSpain

Personalised recommendations