Recommender intelligent system for chronic disease risk management

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.