PREDICTING THE SEVERITY OF THE COURSE AND OUTCOMES OF VIRAL ENCEPHALITIS USING MULTIVARIATE MATHEMATICAL ANALYSIS
Keywords:
Virus, predictive models, medical sciencesAbstract
The relevance of the subject under study lies in the wide distribution, severity of the course, and the frequency of disabling consequences of inflammatory diseases of the central nervous system, which determines the importance of predicting their course and outcome, as the principal component of the diagnostic process. The main purpose of this study was to develop a predictive model of the severity of inflammatory diseases of the central nervous system, using methods of multifactorial mathematical analysis. The basis of the methodological approach in this study was an experimental, practical study of the principles of creating a model based on mathematical analysis methods for predicting the severity of the course and consequences of viral encephalitis. In this paper, results were obtained indicating the effectiveness of the proposed method for predicting and evaluating the course of viral encephalitis in patients of the groups taken for study. Negative results in predicting negative outcomes of this disease according to the proposed method amounted to only 6.7% (6 patients out of 90), while errors in predicting positive outcomes were noted at the level of 5.6% (5 patients out of 90). According to the results of the prognosis, all patients who took part in the examination underwent therapy correction. The duration of administration of neurometabolic drugs was also increased, due to the processing of the results of a multifactorial mathematical analysis. The practical significance of the results obtained in this study lies in the possibility of their implementation in the practice of medical institutions of various profiles to compile predictive models of the course and outcome of inflammatory diseases of the central nervous system
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