A case study on the application of probabilistic conditional modelling and reasoning to clinical patient data in neurosurgery

Authors:
Christoph Beierle;Marc Finthammer;Nico Potyka;Julian Varghese;Gabriele Kern-Isberner
Affiliations:
Dept. of Computer Science, FernUniversität in Hagen, Hagen, Germany;Dept. of Computer Science, FernUniversität in Hagen, Hagen, Germany;Dept. of Computer Science, FernUniversität in Hagen, Hagen, Germany;Dept. of Computer Science, FernUniversität in Hagen, Hagen, Germany;Dept. of Computer Science, TU Dortmund, Dortmund, Germany
Venue:
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2013

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Abstract

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.