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ISSN 2079-3316 Bilingual electronic scientific Electronic scientific journal of the Ailamazyan Program System Institute of the Ailamazyan PSI of PSI of Russian Academy of Science of RAS 12+ 
Volume 16 (2025) . Issue 1 (64) . Paper No. 3 (445)

Medical Informatics

Research Article

The ontology complex as the model of intelligent system to support rehabilitation of patients after stroke

Valeria Viktorovna Gribova1, Valentin Borisovich Shumatov2, Sergey Vasilyevich Lebedev3, Elena Aref'evna Shalfeeva4, Evgenij Yur'evich Shestopalov5, Dmitri Borisovich Okun6Correspondent author, Roman Igorevich Kovalev7, Ekaterina Ivanovna Shepeta8, Leonid Alexandrovich Fedorishchev9, Alexander Yakovlevich Lifshits10

1,4,6,9,10Institute of Automation and Control Processes of the Far Eastern Branch of the RAS, Vladivostok, Russia
2,5,8Federal State Budgetary Educational Institution of Higher Education "Pacific State Medical University" of the Ministry of Healthcare of the Russian Federation, Vladivostok, Russia
6 Dmitri Borisovich Okun — Correspondent author okdm@iacp.dvo.ru

Abstract. The main incentive for the introduction of computer technologies into the healthcare system is the desire to significantly improve the quality of life of people. This includes improving the quality and speed of treatment, reducing the cost of medical services and acquiring effective means to comply with regulatory requirements.

At the present stage of rehabilitation development, the need for active implementation of medical decision support systems and artificial intelligence technologies becomes obvious. These technologies can significantly improve the understanding of the clinical aspects of disorders, the level of activity and participation of stroke patients in the rehabilitation process. A key component of the successful application of these systems is the importance of formalizing knowledge and creating ontologies that provide a structured and connected presentation of medical information and define the rules for their interpretation.

This paper presents a set of interrelated ontological models underlying the intellectual decision support system being developed in the rehabilitation of stroke patients. The IACPaaS cloud platform is used to implement the complex of ontologies. Ontologies and the target resources generated on their basis are the basic elements of the system being developed, which will soon be provided to healthcare professionals to solve urgent rehabilitation issues. Mechanisms are provided for the planned expansion and refinement of the knowledge base, which will allow the system to easily adapt to new medical research results and optimize its work as a whole. (In Russian).

Keywords: medical decision support system, intelligent service, rehabilitation, stroke, knowledge base, ontological approach, knowledge engineering

MSC-20202020 Mathematics Subject Classification 68P05; 92C50, 68T30MSC-2020 68-XX: Computer science
MSC-2020 68Pxx: Theory of data
MSC-2020 68P05: Data structures
MSC-2020 92-XX: Biology and other natural sciences
MSC-2020 92Cxx: Physiological, cellular and medical topics
MSC-2020 92C50: Medical applications (general)
MSC-2020 68Txx: Artificial intelligence
MSC-2020 68T30: Knowledge representation

For citation: Valeria V. Gribova, Valentin B. Shumatov, Sergey V. Lebedev, Elena A. Shalfeeva, Evgenij Yu. Shestopalov, Dmitri B. Okun, Roman I. Kovalev, Ekaterina I. Shepeta, Leonid A. Fedorishchev, Alexander Ya. Lifshits. The ontology complex as the model of intelligent system to support rehabilitation of patients after stroke. Program Systems: Theory and Applications, 2025, 16:1, pp. 61–82. (In Russ.). https://psta.psiras.ru/2025/1_61-82.

Full text of article (PDF): https://psta.psiras.ru/read/psta2025_1_61-82.pdf.

The article was submitted 28.11.2024; approved after reviewing 20.01.2025; accepted for publication 21.02.2025; published online 03.03.2025.

© Gribova V. V., Shumatov V. B., Lebedev S. V., Shalfeeva E. A., Shestopalov E. Yu., Okun D. B., Kovalev R. I., Shepeta E. I., Fedorishchev L. A., Lifshits A. Ya.
2025
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