Online Diagnostic System Based on Bayesian Networks

  • Adam Zagorecki
  • Piotr Orzechowski
  • Katarzyna Hołownia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)


In this paper we present a general medical diagnostic expert system intended to serve as an educational self-diagnostic tool, openly available through the WWW. The system has been designed as an alternative to the common self-diagnosis practice among the general public of searching the Internet, finding the first disease with some matching symptoms, and treating this as a diagnosis, in contrast with the differential diagnosis offered by our system. We discuss the medical knowledge elicitation process, automated generation of Bayesian network models, and the diagnostic process. The system uses a scalable and efficient distributed reasoning engine based on multiple Bayesian networks. An analysis of over 100,000 diagnostic cases is presented. The cases are analyzed based on population characteristics such as age and gender. The results show the need for medical education and highlight the most common problems in non-emergency medical care.


Expert systems Bayesian networks Computer-aided diagnosis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ledley, R.S., Lusted, L.B.: Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science 130(3366), 9–21 (1959)CrossRefGoogle Scholar
  2. 2.
    Warner, H.R., Toronto, A.F., Veasey, L.G., Stephenson, R.: A mathematical approach to medical diagnosis. JAMA: The Journal of the American Medical Association 177(3), 177–183 (1961)CrossRefGoogle Scholar
  3. 3.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann (1988)Google Scholar
  4. 4.
    Middleton, B., Shwe, M., Heckerman, D., Henrion, M., Horvitz, E., Lehmann, H., Cooper, G.: Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. Medicine 30, 241–255 (1991)Google Scholar
  5. 5.
    Lucas, P.: Expert knowledge and its role in learning bayesian networks in medicine: An appraisal. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 156–166. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    D’Ambrosio, B.: Symbolic probabilistic inference in large BN2O networks. In: Proc. Tenth Conf. on Uncertainty in Artificial Intelligence, pp. 128–135 (1994)Google Scholar
  7. 7.
    SMILE: Structural Modeling, Inference, and Learning Engine,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adam Zagorecki
    • 1
  • Piotr Orzechowski
    • 1
  • Katarzyna Hołownia
    • 1
  1. 1.Infermedica, s.c.WrocławPoland

Personalised recommendations