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Design of an Internet-Based Advisory System: A Multi-agent Approach

  • Saadat M. Alhashmi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5357)

Abstract

With the emerging proliferation of information and communications technology in the home and work environments, the provision of computer-based medical advisory systems in healthcare could lead to huge savings to the cost of caring for patients with chronic conditions, such as, diabetes, asthma and hypertension. This paper proposes that an internet-based medical expert system could facilitate a far more efficient system in eliminating the number of unnecessary visits to General Practitioner (GP) for routine consultations. An internet-based intelligent system implementing a variety of functions carried in GP consultations is, thus proposed. The system design is based on multi-agent architecture, which attempts to replicate the roles of each person in a typical GP consultation environment. The role of clinical decision-making is carried out by a fuzzy inference engine linked to a knowledge-base of patient records. The management of diabetes is presented as a case study in the paper.

Keywords

Multi-agent advisory system diabetes fuzzy logic 

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References

  1. 1.
    Phuong, N.H., Kreinovich, V.: Fuzzy logic and its applications in medicine. International journal of medical informatics 62(2-3), 165–173 (2001)CrossRefGoogle Scholar
  2. 2.
    Steimann, F.: Fuzzy Set Theory in Medicine. Artificial Intelligence in Medicine 11, 1–7 (1997)CrossRefGoogle Scholar
  3. 3.
    IDF: Diabetes On The Rise World-Wide, http://www.docguide.com
  4. 4.
    Diabetes Basics - What is Diabetes?, http://www.lifeclinic.com
  5. 5.
    Daly, H., et al.: Diabetes Care: A problem Solving Approach. Heinemann Professional Publishing (1984)Google Scholar
  6. 6.
    Hill, R.D.: Diabetes Health Care. Chapman and Hall, London (1987)Google Scholar
  7. 7.
    Lasker, R.D.: The diabetes control and complications trial. Implications for policy and practice. New England Journal of Medicine 329, 977–986 (1993)Google Scholar
  8. 8.
    Wooldridge, M.: An Introduction to MultiAgent Systems, p. 348. John Wiley & Sons, Ltd., Chichester (2002)Google Scholar
  9. 9.
    Odell, J., Parunak, D.V.H., Bauer, B.: Representing Agent Interaction Protocols in UML. Agent_Oriented Software Engineering, pp. 121–140 (2001)Google Scholar
  10. 10.
    Object Management, G, Unified Modelling Language Specification Version 1.3, in OMG Document (1999)Google Scholar
  11. 11.
    FIPA Communicative Act Library Specification, http://www.fipa.org
  12. 12.
    FIPA Request Interaction Protocol Specification, http://www.fipa.org
  13. 13.
    Wirfs-Brock, R., Wilkinson, B., Wiener, L.: Designing object-oriented software, p. 341. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
  14. 14.
    Bennett, S., McRobb, S., Farmer, R.: Object-Oriented Systems Analysis and Design using UML. McGraw Hill, New York (1999)Google Scholar
  15. 15.
    Frenste, J.H.: Expert Systems and Open Systems. In: Proc. AM Assoc. Medical Systems and InformaticsGoogle Scholar
  16. 16.
    Pham, T.T., Chen, G.: Some Applications of Fuzzy Logic in Rule-Based Expert Systems. Expert Systems 19(4), 208–223 (2002)CrossRefGoogle Scholar
  17. 17.
    Bradshaw, J.M.: Software agents, p. 480. MIT Press, Cambridge (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Saadat M. Alhashmi
    • 1
  1. 1.School of Information Technology, Sunway CampusMonash UniversitySelangorMalaysia

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