Modelling and Reasoning in Biomedical Applications with Qualitative Conditional Logic

Abstract

Different approaches have been investigated for the modelling of real-world situations, especially in the medical field, many of which are based on probabilities or other numerical parameters. In this paper, we show how real world situations from the biomedical domain can be conveniently modelled with qualitative conditionals by presenting three case studies: modelling the classification of certain mammals, modelling infections with the malaria pathogen, and predicting the outcome of chronic myeloid leukaemia. We demonstrate that the knowledge to be modelled can be expressed directly and declaratively using qualitative conditional logic. For instance, it is straightforward to handle exceptions to a general rule as conditionals support nonmonotonic reasoning. Each of the knowledge bases is evaluated with example queries and with respect to different inference mechanisms that have been proposed for conditional knowledge, including p-entailment, system Z, and various inference relations based on c-representations. Comparing the obtained inference results with the answers expected from human experts demonstrates the feasibility of the modelling approach and also provides an empirical evaluation of the employed nonmonotonic inference relations in realistic application scenarios.