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Recommender intelligent system for chronic disease risk management
- Authors: Grigoriev O.G.1, Kobriskii B.A.1, Blagosklonov N.A.1, Ginzburg B.G.2
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Affiliations:
- Federal Research Center “Computer Science and Control” of Russian Academy of Sciences
- K.E. Tsiolkovski KSU Medical Institute
- Issue: No 2 (2023)
- Pages: 27-37
- Section: Information processing and data analysis
- URL: https://journal-vniispk.ru/2071-8632/article/view/286504
- DOI: https://doi.org/10.14357/20718632230203
- ID: 286504
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Abstract
A recommender intelligent system for the problem of health maintenance is presented, which provides the possibility of integral analysis of different types of information to assess the risk levels of chronic diseases and issue personal recommendations to reduce the likelihood or prevent the development of a pathological process. The system is based on a heterogeneous semantic network with a knowledge base solver that uses an argumentative reasoning algorithm. The recommendations given to the users are the practical realization of the modern concept of early detection of predisposition to the development of diseases with the focus on the active involvement of the person in the process of preventive measures. The results of testing the AI-HIPPOCRATES health-saving recommendation system on real data is considered; the possibility of using the proposed approach to assess the risks of chronic diseases (arterial hypertension, myocardial infarction, stroke, depression) based on a questionnaire is shown. At present, further research is aimed at modifying the developed intelligent recommendation system to monitor the psychoemotional state of critical infrastructure operators using remote monitoring data.
About the authors
O. G. Grigoriev
Federal Research Center “Computer Science and Control” of Russian Academy of Sciences
Author for correspondence.
Email: oleggpolikvart@yandex.ru
Руководитель отделения. Доктор технических наук. Область научных интересов: ИИ-методы обработки текстов, извлечение знаний из текстов, рекомендательные системы для здоровьесбережения на основе ИИ-методов, медицинские базы знаний
Russian Federation, MoscowB. A. Kobriskii
Federal Research Center “Computer Science and Control” of Russian Academy of Sciences
Email: kba_05@mail.ru
Заведующий отделом. Доктор медицинских наук. Область научных интересов: инженерия знаний, нечеткая логика, системы интеллектуальной поддержки принятия решений, гибридные системы, рекомендательные системы
Russian Federation, MoscowN. A. Blagosklonov
Federal Research Center “Computer Science and Control” of Russian Academy of Sciences
Email: nblagosklonov@frccsc.ru
Младший научный сотрудник. Область научных интересов: информационные технологии в медицине, системы поддержки принятия решений, искусственный интеллект в медицине
Russian Federation, MoscowB. G. Ginzburg
K.E. Tsiolkovski KSU Medical Institute
Email: bginz@mail.ru
Профессор кафедры. Доктор медицинских наук. Область научных интересов: профилактическая медицина
Russian Federation, KalugaReferences
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