iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/3-540-45497-7_4
Using Bayesian Networks to Model Emergency Medical Services | SpringerLink
Skip to main content

Using Bayesian Networks to Model Emergency Medical Services

  • Conference paper
  • First Online:
Medical Data Analysis (ISMDA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2199))

Included in the following conference series:

  • 773 Accesses

Abstract

Due to the uncertain nature of many of the factors that influence on the performance of an emergency medical service, we propose using Bayesian networks to model this kind of systems. We use an algorithm for learning Bayesian networks to build the model, from the point of view of a hospital manager, and apply it to the specific case of a spanish hospital. We also report the results of some preliminary experimentation with the model.

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
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. S. Acid, L. M. de Campos. An algorithm for finding minimum d-separating sets in belief networks. in: E. Horvitz, F. Jensen (Eds.), Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Mateo, 3–10, 1996.

    Google Scholar 

  2. S. Acid and L. M. de Campos. A hybrid methodology for learning belief networks: BENEDICT. Int. J. Approx. Reason., 27(3):235–262, 2001.

    Article  MATH  Google Scholar 

  3. S. Acid and L. M. de Campos. An algorithm for learning probabilistic belief networks using minimum d-separating sets. Submitted to J. Artif. Intell. Res.

    Google Scholar 

  4. G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn., 9(4):309–348, 1992.

    MATH  Google Scholar 

  5. R. Duda, P. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, New York, 1973.

    MATH  Google Scholar 

  6. D. Heckerman, D. Geiger, D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn., 20:197–243, 1995.

    MATH  Google Scholar 

  7. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, 1988.

    Google Scholar 

  8. J. R. Quinlan. C4.5 Programs for Machine Learning. Morgan Kaufmann, 1993.

    Google Scholar 

  9. P. Spirtes, C. Glymour, R. Scheines. Causation, Prediction and Search. Lecture Notes in Statistics 81. Springer Verlag, New York, 1993.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Acid, S., de Campos, L.M., Rodríguez, S., Rodríguez, J.M., Salcedo, J.L. (2001). Using Bayesian Networks to Model Emergency Medical Services. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_4

Download citation

  • DOI: https://doi.org/10.1007/3-540-45497-7_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42734-6

  • Online ISBN: 978-3-540-45497-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics