Journal "Software Engineering"
a journal on theoretical and applied science and technology
ISSN 2220-3397

Issue N6 2024 year

DOI: 10.17587/prin.15.308-321
Knowledge Graph Formation Using LLM for Disease Treatment in Decision Support Systems
V. V. Gribova, D. Sc. (Technical Sciences), Deputy Director, Head of intelligent systems lab, gribova@iacp.dvo.ru, Institute of Automation and Control Processes, Far Eastern Branch of Russian, Academy of Sciences, Vladivostok, 690041, Russian Federation, Far Eastern Federal University, Vladivostok, 690922, Russian Federation, V. S. Perevolotsky, Postgraduate Student, lost.yayo@gmail.com, Far Eastern Federal University, Vladivostok, 690922, Russian Federation
Corresponding author:Vladimir S. Perevolotsky, Postgraduate Student, Far Eastern Federal University, Vladivostok, 690922, Russian Federation, E-mail: lost.yayo@gmail.com
Received on February 13, 2024
Accepted on March 26, 2024

The article presents an advanced method for creating knowledge graphs, applicable in medical decision support systems. It focuses on adapting the method to various clinical guidelines, making it particularly valuable in the medi¬cal field. The developed universal ontological pattern allows for the formation of knowledge bases for a wide range of diseases, making the process flexible and scalable without the need for specific programming. This approach significantly reduces the labor intensity of creating medical support systems and facilitates their integration and adaptation to various clinical conditions and requirements. The practical significance of the method was confirmed in the management systems for the therapy of various diseases, including allergic rhinitis, underscoring its universality and applicability in the current medical context. The effectiveness of the proposed approach opens new perspectives for the development and improvement of medical information systems, contributing to the improvement of patient care and treatment quality. Thus, the article makes a significant contribution to the advancement of information technology in medicine, offering a solution that can be adapted for a wide range of medical applications and research.

Keywords: artificial intelligence, clinical decision support systems, knowledge graphs, ontology, personalized treatment, treatment management automation, machine learning in medicine, LLM
pp. 308—321
The work was carried out within the framework of the state assignment of the Institute of Automated Control of the Far Eastern Branch of the Russian Academy of Sciences (topic FWFW-2021-0004 — an approach for translating models of regulatory documents into a machine-understandable representation using LLM).
For citation:
Gribova V. V., Perevolotsky V. S. Knowledge Graph Formation Using LLM for Disease Treatment in Decision Support Systems, Programmnaya Ingeneria, 2024, vol. 15, no. 6, pp. 308—321. DOI: 10.17587/prin.15.308-321.
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