Computations in Rough-Granular Computing (RGC) are performed on (information) granules. The rough set approach is used in RGC for inducing granules approximating other granules about which imperfect knowledge is given only. For modeling of complex systems, it is important to extend the RGC approach to Interactive Rough-Granular Computing (IRGC) based on interactions of granules. In this paper, we discuss some fundamental issues for interaction of granules such as general scheme of interactions and the role of dynamic attributes and dynamic information systems in modeling interactive computations.
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We discuss medical treatment planning in the context of case-based planning, where plans (of treatment) are treated as complex decisions. A plan for a particular case is constructed from known plans for similar training examples. In order to evaluate and improve the prediction quality of complex decisions, we use a method for approximation of similarity measure between plans. The method makes it possible to transform the acquired domain knowledge about similarities of plans, expressed by medical experts in natural language, to a low level language understandable by the system. To accomplish this task, we developed a method for approximation of the ontology of concepts expressed by medical experts. We present two applications of the ontology approximation, namely, for approximation of similarity between patient histories and for approximation of compatibility of patient histories with planned therapies. Next, we use these concept approximations to define two measures on which are based two methods for (plan) therapy prediction. The article includes results of experiments with these methods performed on medical data obtained from Neonatal Intensive Care Unit, First Department of Pediatrics, Polish-American Institute of Pediatrics, Collegium Medicum, Jagiellonian University, Kraków, Poland. The experiments are pertained to the identification of infants' death risk caused by respiratory failure.
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