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JOURNALS // Artificial Intelligence and Decision Making // Archive

Artificial Intelligence and Decision Making, 2015 Issue 3, Pages 10–17 (Mi iipr327)

This article is cited in 4 papers

Modeling of creative thinking

Cognitive approach to the construction linguistic-image model of knowledge representation for medical intelligent systems

B. A. Kobrinskiiab

a Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow
b Russian National Research Medical University named after N. I. Pirogov

Abstract: In intelligent medical systems traditional linguistic and logical models do not provide a clear comparison between expert cognitive performance and in linguistic form of display holistic visual images of the disease in knowledge base. Meanwhile, at medical diagnostics, in particular for hereditary diseases often important an appearance of the patient. However, it should be noted the absence of well-developed methods and tools to support specific models that provide the use of a single formalism of knowledge represented in verbal and imaginative forms. At present domestic and foreign research and development in this area, for the most part, aimed at solving specific problems, focused on any one side of the problem. Meanwhile, clinical genetics is an area the creation of systems based on a combination of verbalized and imaginative knowledge may provide a way out on a new level of diagnosis. Awareness of these factors is the reason for the increased interest in recent years to the image analysis in medicine in the leading countries of the world (US, UK, and others.). These developments are not targeted on the use of expert knowledge. In the same time, a possible decision may be to build a hybrids linguistic and image frame or ontological models that integrate are qualitative and visually-imaginative indicators for the formal description of diagnostically important clinical manifestations of disease.

Keywords: knowledge engineering, cognitive imaging, cognitive linguistic-and-imaging models, frame, ontology, knowledge base, intelligent system, medicine diagnostic, hereditary diseases.


 English version:
, 2015, 43:5-6, 289–295

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