Skip to main content

Research on Applications of Multi-Agent System Based on Execution Engine in Clinical Decision-Making

  • Conference paper
Health Information Science (HIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8423))

Included in the following conference series:

  • 1163 Accesses

Abstract

Medical errors have become a common concern of social problems. One important reason is the lack of clinical experience. Clinical guidelines are the solution to this problem; however, they are always kept being updated. Let the system adapt to the changing clinical guidelines is a challenging idea. In this paper we propose MAS (Multi-Agent System) based on the Execution Engine can achieve this goal. In the MAS, clinical guidelines are mapped into rules, which are stored in the rule repository and could be processed by the Execution Engine, to guide agents’ behaviors. Rules in the system are configurable and Execution Engine can always obtain the latest data at runtime without interrupting system running, so it implements the system’s adaptability. Agents, which could be deployed in distributed environments [2], simulate doctors’ roles to do aided diagnosis by collaborations which implements data sharing and improves the accuracy of decision-making greatly.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Xiao, L., Cousins, G., Fahey, T., et al.: Developing a rule-driven clinical decision support system with an extensive and adaptative architecture. In: 2012 IEEE14th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 250–254. IEEE (2012)

    Google Scholar 

  2. Xiao, L., Greer, D.: Adaptive agent model: Software adaptivity using an agent-oriented model-driven architecture. Information and Software Technology 51(1), 109–137 (2009)

    Article  Google Scholar 

  3. Patkar, V., Hurt, C., Steele, R., et al.: Evidence-based guidelines and decision support services: a discussion and evaluation in triple assessment of suspected breast cancer. British Journal of Cancer 95(11), 1490–1496 (2006)

    Article  Google Scholar 

  4. Séroussi, B., Bouaud, J., Gligorov, J., et al.: Supporting multidisciplinary staff meetings for guideline-based breast cancer management: a study with OncoDoc2. In: AMIA Annual Symposium Proceedings of the American Medical Informatics Association, vol. 2007, p. 656 (2007)

    Google Scholar 

  5. Bellifemine, F., Poggi, A., Rimassa, G.: Developing multi-agent systems with JADE. In: Castelfranchi, C., Lespérance, Y. (eds.) Intelligent Agents VII. LNCS (LNAI), vol. 1986, pp. 89–103. Springer, Heidelberg (2001)

    Google Scholar 

  6. Ahmed, I., Nazir, R., Chaudhary, M.Y., Kundi, S.: Triple assessment of breast lumps. J. Coll. Physicians Surg. Pak. 17(9), 535 (2007)

    Google Scholar 

  7. Drew, P.J., Chatterjee, S., Turnbull, L.W., et al.: Dynamic contrast enhanced magnetic resonance imaging of the breast is superior to triple assessment for the pre-operative detection of multifocal breast cancer. Annals of surgical oncology 6(6), 599–603 (1999)

    Article  Google Scholar 

  8. http://jade.tilab.com/

  9. http://www.openclinical.net/demos/triple-assessment-guideline-for-secondary-care.html

  10. Bellifemine, F., Poggi, A., Rimassa, G.: Developing multi-agent systems with a FIPA-compliant agent framework. Software-Practice and Experience 31(2), 103–128 (2001)

    Article  MATH  Google Scholar 

  11. Xiao, L., Fox, J., Zhu, H.: Developing an Open and Adaptive Agent Architecture to Support Multidisciplinary Decision Making

    Google Scholar 

  12. Xiao, L., Greer, D.: Environment support for the configuration of adaptive agents. Multiagent and Grid Systems 5, 1–23 (2009)

    Google Scholar 

  13. Xiao, L., Fox, J., Zhu, H.: An Agent-oriented Approach to Support Multidisciplinary Care Decisions. In: 2013 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems (ECBS-EERC), pp. 8–17. IEEE (2013)

    Google Scholar 

  14. Xiao, L., Hu, B., Croitoru, M., et al.: A knowledgeable security model for distributed health information systems. Computers & security 29(3), 331–349 (2010)

    Article  Google Scholar 

  15. Musen, M.A., Shahar, Y., Shortliffe, E.H.: Clinical Decision-Support Systems Biomedical Informatics, pp. 698–736. Springer, New York (2006)

    Google Scholar 

  16. Harold, E.R.: Processing X M L. with Java: a Guide to SAX, DOM, JDOM, JAXP, and TrAX (2003)

    Google Scholar 

  17. Hunter, J.: JDOM makes XML easy. In: Java Developer Conference on Sun’s 2002 Worldwide (2002)

    Google Scholar 

  18. Garg, A.X., Adhikari, N.K.J., McDonald, H., et al.: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. JAMA: The Journal of the American Medical Association 293(10), 1223–1238 (2005)

    Article  Google Scholar 

  19. Pestotnik, S.L., Classen, D.C., Evans, R.S., et al.: Implementing antibiotic practice guidelines through computer-assisted decision support: clinical and financial outcomes. Annals of Internal Medicine 124(10), 884–890 (1996)

    Article  Google Scholar 

  20. Bellifemine, F., Poggi, A., Rimassa, G.: JADE–A FIPA-compliant agent framework. In: Proceedings of PAAM 99(97-108): 33 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Yan, Z. et al. (2014). Research on Applications of Multi-Agent System Based on Execution Engine in Clinical Decision-Making. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06269-3_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06268-6

  • Online ISBN: 978-3-319-06269-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics