Abstract
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 of the treatment of newborn infants with respiratory failure. The reported experiments were performed on medical data obtained from the Neonatal Intensive Care Unit in the Department of Pediatrics, Collegium Medicum, Jagiellonian University. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Bazan, J. G., Kruczek, P., Bazan-Socha, S., Skowron, A., & Pietrzyk, J. J. (2006). Automatic planning of treatment of infants with respiratory failure through rough set modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4259 LNAI, pp. 418–427). Springer Verlag. https://doi.org/10.1007/11908029_44
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