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We discuss an application of rough set tools for modeling networks of classifiers induced from data and ontology of concepts delivered by experts. Such networks allow us to develop strategies for automated planning of a treatment of infants with respiratory illness. We report results of experiments with the networks of classifiers used in automated planning(More)
X-linked lissencephaly with ambiguous genitalia syndrome (XLAG) (OMIM #3000215) is a rare, severe malformation of the brain cortex with abnormal neuronal migration caused by mutations of the ARX gene. All the reported patients with lissencephaly are males who presented with a posterior-to-anterior gradient, moderately increased thickness of the brain(More)
The prognostic value of some continuos measurement parameters calculating Bronchopulmonary Dysplasia predictor is the main goal of the paper. The most important question is, if the continuous measurement of parameters can help us to build a better algorithm solving Bronchopulmonary Dysplasia prediction problem?
Many profitable methods of damage detection in rotating machines involve cyclostationarity. This approach exploits the fact that vibrations of a damaged machinery possess a periodic envelope. The frequency related to this periodic behavior is strictly related to one of the characteristic frequencies associated with design and specific operation of the(More)
The main goal of this paper is prediction of Bronchopulmonary Dysplasia among newborn children. Static and dynamic parameters obtained from hospital database and medical data monitoring system were used to build logistic regression and radial basis function neural networks (RBFN) models.
The problem considered is how to model perception and identify behavioral patterns of objects changing over time in complex dynamical systems. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzisław Pawlak during the early 1980s provides the foundation for the construction of(More)
This paper investigates medical planning in the context of a complex dynamical system. The solution to the problem of medical planning is proposed using application of rough set tools for modeling networks of classifiers induced from data and ontology of concepts delivered by experts. Such networks allow us to develop strategies for automated planning of a(More)
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