A Case Study on the Application of Probabilistic Conditional Modelling and Reasoning to Clinical Patient Data in Neurosurgery

  • Christoph Beierle
  • Marc Finthammer
  • Nico Potyka
  • Julian Varghese
  • Gabriele Kern-Isberner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7958)


We present a case-study of applying probabilistic logic to the analysis of clinical patient data in neurosurgery. Probabilistic conditionals are used to build a knowledge base for modelling and representing clinical brain tumor data and expert knowledge of physicians working in this area. The semantics of a knowledge base consisting of probabilistic conditionals is defined by employing the principle of maximum entropy that chooses among those probability distributions satisfying all conditionals the one that is as unbiased as possible. For computing the maximum entropy distribution we use the MEcore system that additionally provides a series of knowledge management operations like revising, updating and querying a knowledge base. The use of the obtained knowledge base is illustrated by using MEcore’s knowledge management operations.


Epistemic State Belief Revision Propositional Variable Belief Change Hypothetical Reasoning 
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  1. 1.
    Alchourrón, C.E., Gärdenfors, P., Makinson, P.: On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic 50(2), 510–530 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Beierle, C., Kern-Isberner, G.: A conceptual agent model based on a uniform approach to various belief operations. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS (LNAI), vol. 5803, pp. 273–280. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Bruch, H.P., Trentz, O.: Berchthold Chirurgie, 6. Auflage. Elsevier GmbH (2008)Google Scholar
  4. 4.
    Csiszár, I.: I-divergence geometry of probability distributions and minimization problems. Ann. Prob. 3, 146–158 (1975)zbMATHCrossRefGoogle Scholar
  5. 5.
    Darwiche, A., Pearl, J.: On the logic of iterated belief revision. Artificial Intelligence 89, 1–29 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Dubois, D., Prade, H.: Focusing vs. belief revision: A fundamental distinction when dealing with generic knowledge. In: Nonnengart, A., Kruse, R., Ohlbach, H.J., Gabbay, D.M. (eds.) FAPR 1997 and ECSQARU 1997. LNCS, vol. 1244. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  7. 7.
    Finthammer, M., Beierle, C., Berger, B., Kern-Isberner, G.: Probabilistic reasoning at optimum entropy with the MEcore system. In: Lane, H.C., Guesgen, H.W. (eds.) Proc. FLAIRS 2009. AAAI Press, Menlo Park (2009)Google Scholar
  8. 8.
    Gärdenfors, P.: Knowledge in Flux: Modeling the Dynamics of Epistemic States. MIT Press, Cambridge (1988)zbMATHGoogle Scholar
  9. 9.
    Hosten, N., Liebig, T.: Computertomografie von Kopf und Wirbelsäule. Georg Thieme Verlag (2007)Google Scholar
  10. 10.
    Katsuno, H., Mendelzon, A.O.: On the difference between updating a knowledge base and revising it. In: Proceedings Second International Conference on Principles of Knowledge Representation and Reasoning, KR 1991, pp. 387–394. Morgan Kaufmann, San Mateo (1991)Google Scholar
  11. 11.
    Kern-Isberner, G.: Characterizing the principle of minimum cross-entropy within a conditional-logical framework. Artificial Intelligence 98, 169–208 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Kern-Isberner, G.: Conditionals in nonmonotonic reasoning and belief revision. LNCS (LNAI), vol. 2087. Springer, Heidelberg (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Kern-Isberner, G.: Linking iterated belief change operations to nonmonotonic reasoning. In: Brewka, G., Lang, J. (eds.) Proceedings 11th International Conference on Knowledge Representation and Reasoning, KR 2008, pp. 166–176. AAAI Press, Menlo Park (2008)Google Scholar
  14. 14.
    Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., Burger, P.C., Jouvet, A., Scheithauer, B.W., Kleihues, P.: The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathologica 114(2), 97–109 (2007)CrossRefGoogle Scholar
  15. 15.
    Müller, M.: Chirurgie für Studium und Praxis, 9. Auflage. Medizinische Verlags-und Informationsdienste (2007)Google Scholar
  16. 16.
    Paris, J.B., Vencovska, A.: In defence of the maximum entropy inference process. International Journal of Approximate Reasoning 17(1), 77–103 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Park, B.J., Kim, H.K., Sade, B., Lee, J.H.: Epidemiology. In: Lee, J.H. (ed.) Meningiomas: Diagnosis, Treatment, and Outcome, p. 11. Springer (2009)Google Scholar
  18. 18.
    Rödder, W., Reucher, E., Kulmann, F.: Features of the expert-system-shell SPIRIT. Logic Journal of the IGPL 14(3), 483–500 (2006)zbMATHCrossRefGoogle Scholar
  19. 19.
    Shore, J.E.: Relative entropy, probabilistic inference and AI. In: Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence, pp. 211–215. North-Holland, Amsterdam (1986)Google Scholar
  20. 20.
    Steiger, H.-J., Reulen, H.J.: Manual Neurochirurgie. Ecomed Medizin (2006)Google Scholar
  21. 21.
    Varghese, J.: Using probabilistic logic for the analyis and evaluation of clinical patient data in neurosurgery. B.Sc. Thesis, Univ. Hagen (2012) (in German)Google Scholar
  22. 22.
    Varghese, J., Beierle, C., Potyka, N., Kern-Isberner, G.: Using probabilistic logic and the principle of maximum entropy for the analysis of clinical brain tumor data. In: Proc. CBMS 2013. IEEE Press, New York (to appear 2013)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christoph Beierle
    • 1
  • Marc Finthammer
    • 1
  • Nico Potyka
    • 1
  • Julian Varghese
    • 1
  • Gabriele Kern-Isberner
    • 2
  1. 1.Dept. of Computer ScienceFernUniversität in HagenHagenGermany
  2. 2.Dept. of Computer ScienceTU DortmundDortmundGermany

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