Architecture of the patient's health predicting system using agent-based modeling and machine learning methods

The introduction of information technology in medical institutions contributes to the development of predictive, preventive and personalized medicine. The task that arises in this case is to create a software analogue of the patient, capable of taking into account his individual indicators and predicting the state of health, is still relevant. The architecture of the patient's health predicting system presented in the work is aimed at solving this problem. A distinctive feature of the system architecture is the combination of the principles of agent modeling and representation of the patient's body in the form of interacting modules, which opens up wide opportunities for modeling the health status of the patient's body. The paper describes the hierarchy of agents in the system architecture, describes the rules of agent interaction and provides a mathematical model for evaluating the effectiveness of therapeutic effects on the patient's body, the solution of which is achieved through the interaction of system agents. The prediction of the patient's health status is performed using downloadable pre-trained machine learning models, while the models are directly involved in determining the behavior of agents. The architecture of the patient's health predicting system, implemented in the form of a software package, is a powerful tool for building agent-based predicting models aimed at modeling physiological and pathological processes and effects on the patient's body.

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