Skip to main content

A Reasoning Module for Distributed Clinical Decision Support Systems

  • Conference paper
  • First Online:
Intelligent Distributed Computing IX

Part of the book series: Studies in Computational Intelligence ((SCI,volume 616))

  • 760 Accesses

Abstract

One of the main challenges in distributed clinical decision support systems is to ensure that the flow of information is kept. The failure of one or more components should not bring down an entire system. Moreover, it should not impair any decision processes that are taking place in a functioning component. This work describes a decision module that is capable of managing states of incomplete information which result from the failure of communication between components or delays in making the information available. The framework is also capable of generating scenarios for situations in which there are information gaps. The proposal is described through an example about colon cancer staging.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Benson, A., Bekaii-Saab, T., Chan, E., Chen, Y.J., Choti, M., Cooper, H., Engstrom, P.: NCCN Clinical Practice Guideline in Oncology Colon Cancer. Technical Report, National Comprehensive Cancer Network (2009)

    Google Scholar 

  2. Boxwala, A.A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q.T., Wang, D., Patel, V.L., Greenes, R.A., Shortliffe, E.H.: GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J. Biomed. Inf. 37(3), 147–161 (2004)

    Article  Google Scholar 

  3. Fox, J., Ma, R.T.: Decision support for health care : the PROforma evidence base. Inf. Prim. Care 14(1), 49–54 (2006)

    Google Scholar 

  4. Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer-Verlag, New York (2009)

    Book  MATH  Google Scholar 

  5. Hosobe, H., Satoh, K., Codognet, P.: Agent-Based speculative constraint processing. IEICE Trans. Inf. Syst. E90-D(9), 1354–1362 (2007)

    Google Scholar 

  6. Hua, Z., Gong, B., Xu, X.: A dsahp approach for multi-attribute decision making problem with incomplete information. Expert Syst. Appl. 34(3), 2221–2227 (2008)

    Article  Google Scholar 

  7. Kononenko, I.: Inductive and bayesian learning in medical diagnosis. Appl. Artif. Intell. 7(4), 317–337 (1993)

    Article  Google Scholar 

  8. Korb, K., Nicholson, A.: Bayesian Artifical Intelligence, 2nd edn. CRC Press, London (2003)

    Book  Google Scholar 

  9. Oliveira, T., Novais, P., Neves, J.: Representation of clinical practice guideline components in owl. In: Trends in Practical Applications of Agents and Multiagent Systems, Advances in Intelligent Systems and Computing, vol. 221, pp. 77–85. Springer (2013)

    Google Scholar 

  10. Peleg, M.: Computer-Interpretable clinical guidelines: a methodological review. J. Biomed. Inf. 46(4), 744–763 (2013)

    Article  Google Scholar 

  11. Shortliffe, E.H., Davis, R., Axline, S.G., Buchanan, B.G., Green, C., Cohen, S.N.: Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the mycin system. Comput. Biomed. Res. 8(4), 303–320 (1975)

    Article  Google Scholar 

  12. Straszecka, E.: Combining uncertainty and imprecision in models of medical diagnosis. Inf. Sci. 176(20), 3026–3059 (2006)

    Article  MathSciNet  Google Scholar 

  13. Tu, S.W., Campbell, J.R., Glasgow, J.: Nyman, M.a., McClure, R., McClay, J., Parker, C., Hrabak, K.M., Berg, D., Weida, T., Mansfield, J.G., Musen, M.a., Abarbanel, R.M.: The SAGE guideline model: achievements and overview. J. Am. Med. Inf. Assoc. JAMIA 14(5), 589–98 (2007)

    Google Scholar 

  14. Van der Heijden, M., Lucas, P.J.F.: Describing disease processes using a probabilistic logic of qualitative time. Artif. Intell. Med. 59(3), 143–155 (2013)

    Article  Google Scholar 

  15. Visscher, S., Lucas, P.J.F., Schurink, C.A.M., Bonten, M.J.M.: Modelling treatment effects in a clinical Bayesian network using boolean threshold functions. Artif. Intell. Med. 46(3), 251–266 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tiago Oliveira .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Oliveira, T., Satoh, K., Novais, P., Neves, J., Leão, P., Hosobe, H. (2016). A Reasoning Module for Distributed Clinical Decision Support Systems. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25017-5_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25015-1

  • Online ISBN: 978-3-319-25017-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics