MATHEMATICAL SUPPORT OF THE INTELLIGENT INFORMATION SYSTEM OF ASSESSING THE OBJECT STATE
AbstractAt present, information technologies (IT) are intensively used all over the world in various sectors, and today medical institutions cannot do without them when organizing the process of medical diagnostic. The IT efficiency is determined by the degree of their intellectualization that is by including knowledge bases as their component and by the transition from data processing to the processing of knowledge. The efficiency of making decisions in various areas of activity is determined by the quality and quick delivery of information. Medicine constitutes no exception in this sense. The advanced level of computer technology, applied tools, diagnostics on the basis of automated systems of decision support made it possible to solve the tasks of assessing the state of the object at a qualitatively new level. The subject matter of this study is to ensure the mathematical support of the intelligent information system (IS) of assessing the state of the object. The object is understood as a patient who came through a myocardial infarction (MI). The goal of the study is to develop mathematical support of the intelligent IS of assessing and predicting a patient’s condition. To achieve the stated goal, the following tasks were solved: statistically valid and uncorrelated signs were specified; these signs enable distinguishing the group of patients who survived from those who died, “decisive rules” were formulated for predicting the MI clinical outcome. In the process of the study, the mathematical IT of assessing the state of the object was developed. The following result was obtained: the suggested mathematical models for predicting the outcome of myocardial infarction that were developed with the use of the method of discriminant function and took into account human blood values can prevent sudden coronary death and improve the diagnostic efficiency. Conclusions. Mathematical models were developed to predict the state of the object in the event of uncertainty. The use of the developed mathematical models enables improving the accuracy of predicting the state of the object in a real-time environment and in the early stages of the disease development by 4.2% and 10%, and applying adequate preventive and therapeutic and rehabilitation measures in time as well as preventing sudden coronary death. The developed mathematical models were tested.
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