Abstract
Background. Prediction of life-threatening arrhythmias is necessary to analyze the patient's heart condition, anticipate cardiovascular complications (ACC), identify the main problems in the functioning of the patient's heart and assess the risk of ACC. The introduction of medical decision support systems as part of the information and measurement system of electrocardiodiagnostics is an urgent task aimed at identifying and analyzing risk factors of and predicting. The purpose of the article is to develop algorithms for detecting life-threatening arrhythmias and to assess the risk of cardiovascular complications. Materials and methods. Algorithms for detecting life-threatening arrhythmias such as atrioventricular block, hemodynamically significant arrhythmia, and the re-entry mechanism are considered. Results and conclusions. It is proposed to use multi-agent technologies for the implementation of the considered algorithms, which make it possible to increase the efficiency of the information and measurement system of electrocardiodiagnostics and optimize the provision of therapeutic and diagnostic medical care from the point of view of individualization. An agent-based approach to the construction of an information and measurement system of electrocardiodiagnostics allows us to functionally distribute the tasks solved by individual intelligent agents (IA), and then integrate the results obtained. A stratification by risk groups of life-threatening arrhythmias has been developed, according to which patients are divided into the following groups: high risk, moderate risk, low risk.