Currently, virtual simulators are being actively developed and implemented to solve problems of a wide profile. A special place among them is occupied by solutions based on virtual reality and biofeedback. The use of an ontological approach and semantic representation of information makes it possible to optimize the processing, storing, searching and analyzing data for the implementation of virtual simulators at all stages of their life cycle. The basic principles of creating virtual simulators with biofeedback, which are the basis of the software package for their creation, reproduction and maintenance, are presented. The implementation of virtual simulators requires a description of the virtual environment, the creation of a work scenario and managing it during operation based on individual user parameters. A complex of ontologies for virtual simulators with biofeedback is proposed, which consists of an ontology of the virtual environment, an ontology of research, an ontology of the user's map, an ontology of equipment and an ontology of knowledge about the user's states. It is shown that the use of this complex of ontologies significantly reduces labor costs when designing virtual simulators and to form an information representation of the solutions being developed, to simplify the creation and introduction of changes. To demonstrate the solutions presented, an example of a virtual simulator for performing work at height is presented, with the help of which it is possible to test a person for acrophobia (fear of heights) and prevention of anxiety disorders, with the possibility of changing the parameters of the virtual environment depending on the user's condition.

/pdf/complex-of-ontologies-for-virtual-simulators-with-3nyimuxc.pdf

Complex of ontologies for virtual simulators with biofeedback

Issues of intelligent individual education are of particular relevance today. This paper describes some aspects of building an individual strategy of education and training of young specialists with the help of tutoring integrated expert systems, which have been developed and used in the educational process of MEPhI for many years.

Some Aspects of Individual Approach to Learning Based on Tutoring Integrated Expert Systems

Gout belongs to the group of microcrystalline arthritides and is a systemic tophus manifested by infl ammation of various tissues caused by deposition of monosodium urate crystals. Verifi cation of gout diagnosis in Russia takes on average four to eight years from the beginning of the disease. This period is suffi cient for the development of various complications (for example, gouty nephropathy) and disability of patient, and, therefore, it is necessary to revise the existing strategy for the diagnosis of this disease. One of the options for improving the provision of medical care can be attributed to the creation of a clinical decision support system (CDSS), which is based on the knowledge of experts, formalized as a knowledge base (KB).Aim. To develop a KB structure for CDSS for gout diagnosis.Material and Methods. Clinical information for gout diagnosis, presented as a nomenclature of medical concepts and logical schemes, which were collected on the basis of federal clinical recommendations, various literature sources and expert knowledge were used as materials. The ontological method was used as a method of knowledge structuring. Combined semantic network and frames were used as the methods of representation.Results. While developing KB structure, a combination of two methods of knowledge representation including semantic network and frames was used. An ontological approach was used in terms of knowledge structuring. The structure was built on the clinical knowledge collected in cooperation with experts in gout diagnosis. Compared with similar developments of diagnosing diseases based on knowledge engineering methods, the main feature of developed KB structure was the use of a separate type “Syndrome” concept as an aggregator accepted in medicine, signifi cantly reducing the volume of KB for diseases.Conclusion. The KB structure was developed comprising the use of seven types of concepts and 11 types of relationships. The structure involved the use of ontological approach and combination of two models of knowledge representation, namely: a semantic network and a frame model.

/pdf/development-of-knowledge-base-structure-for-gout-diagnosis-cjf2sevm.pdf

Development of knowledge base structure for gout diagnosis

Towards designing knowledge bases for aircraft malfunctions diagnostics based on model transformations

Исследуется автоматизация проведения полного цикла диагностики и
 дифференциальной диагностики воспалительных заболеваний сердца и
 назначения персонифицированного лечения. Методом исследования является
 формирование декларативных баз знаний и объясняющего свои результаты
 решателя по единой онтологии. Онтологический решатель интерпретирует
 формализованные знания при получении сведений о новом медицинском случае
 (пациенте).
 Описаны общие принципы разработки и концептуальная архитектура
 интеллектуального сервиса с декларативными знаниями. Сформированы
 информационные компоненты для воспалительных заболеваний сердца и
 программные компоненты для полного цикла диагностики и дифференциальной
 диагностики, персонифицированного лечения. Указаны источники знаний и
 проведено тестирование на случаях из практики, описанных в литературе.
 Приведено обоснование выбора технологий и алгоритмов, выявлены и
 сформулированы требования к программному комплексному сервису, шаги
 по разработке всех компонентов.
 Сервис поддержки принятия диагностических решений со свойствами
 объяснимого искусственного интеллекта в кардиологии реализован на
 медицинском портале облачной платформы IACPaaS. Платформа позволяет
 масштабировать предложенное решение и обеспечивает доступ практикующих
 врачей со свободной регистрацией для экспериментов в реальных
 ситуациях.

Cloud service for differential diagnosis and personalized treatment of inflammatory heart diseases

/pdf/medical-diagnosis-ontology-for-intelligent-decision-support-1kc8hcm3yd.pdf

Medical diagnosis ontology for intelligent decision support systems

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Medical diagnosis ontology for intelligent decision support systems