Generating explanations of pathophysiological system behaviors from qualitative simulation of compartmental models

  • Liliana Ironi
  • Mario Stefanelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)


This paper describes a method for generating, in a language that is comprehensible by the user, explanations of the behaviors of a pathophysiological system from compartmental models. Such behaviors are obtained through the qualitative simulation of a model of the pathophysiological system, which has been built within the QCMF (Qualitative Compartmental Modeling Framework) framework and directly coded into the QSIM language. The main advantage offered by this explanation facility over the simulation outcome plots consists in its capability of explaining how and why the predicted behavior arises from the structure of the modeled system and physical laws. The basic idea underlying the explanation algorithm consists in determining a causal concatenation of the changes from a state to its successor: the algorithm when comparing two successive states will single out those changes that are a direct consequence of the direction of change in the state and will propagate the effects of these changes following the causal order between variables which is implicitely defined by the compartmental structure.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Atkins, G.L.: Multicompartmental Models in Biological Systems. (Chapman and Hall, London, 1974)Google Scholar
  2. 2.
    Carson, E. R., Cobelli, C., Finkenstein, L.: The Mathematical Modeling of Metabolic and Endocrine Systems. (Wiley, New York, 1983)Google Scholar
  3. 3.
    De Kleer, J., Brown, J. S.: Theories of causal ordering. Artificial Intelligence 29 (1986) 33–61CrossRefGoogle Scholar
  4. 4.
    Gautier, P. O., Gruber, T.R.: Generating explanations of device behavior using compositional modeling and causal ordering. Working Papers QR 93, Seattle (1993) 89–97Google Scholar
  5. 5.
    Ironi, L., Stefanelli, M.: A framework for building and simulating qualitative models of compartmental systems. Computer Methods and Programs in Biomedicine 42 (1994) 233–254PubMedGoogle Scholar
  6. 6.
    Iwasaki, Y., Simon, H.A.: Causality in device behavior. Artificial Intelligence 29 (1986) 3–32CrossRefGoogle Scholar
  7. 7.
    Iwaski, Y., Simon, H.A.: Theories of causal ordering: reply to De Kleer and Brown. Artificial Intelligence 29 (1986) 63–72CrossRefGoogle Scholar
  8. 8.
    Iwaski, Y., Simon, H. A.: Causality and model abstraction. Artificial Intelligence 67 (1994) 143–194CrossRefGoogle Scholar
  9. 9.
    Kuipers, B. J.: Qualitative simulation. Artificial Intelligence 29 (1986) 280–338CrossRefGoogle Scholar
  10. 10.
    Lee, M., Compton, P.: Context-dependent causal explanations. Working Papers QR 94, Nara (1994) 176–186Google Scholar
  11. 11.
    Rickel, J., Porter, B.: Automated modeling for answering prediction questions: selecting the time scale and system boundary. Proc. Twelfth National Conference on Artificial Intelligence (AAAI-94), (AAAI/MIT Press, 1994) 1191–1198Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Liliana Ironi
    • 1
  • Mario Stefanelli
    • 2
  1. 1.Istituto di Analisi Numerica del C.N.R.PaviaItaly
  2. 2.Dipartimento di Informatica e Sistemistica dell'Università di PaviaPaviaItaly

Personalised recommendations