So far, the fact that statistically significance differences between samples (sets of parameters of the body xi) before the treatment and after the treatment led to the conclusion of non effective treatment. However, in the framework of the theory of chaos and self-organization the assessment of the significant differences can be carried out without going through statistical methods, based on the analysis parameters of quasi-attractors or using neural emulators. In this report the authors present examples of uncertainties of the 1st kind in regenerative medicine and introduce new technologies to resolve these uncertainties. A procedure for finding differences between samples and determining the parameters of the order (the most important diagnostic features) based methods neurocomputing. The effectiveness of this approach in the evaluation of the effectiveness treatment effects of abnormalities in the body during acute stroke in a kinesotherapy is presented. The authors proved ineffective stochastics and opportunities neurocomputing in the task of system synthesis
Evaluating the effectiveness of treatment of a disease (in this paper is kinesotherapy treatment of acute cerebrovascular accidents and physical therapy for hypertension in the North of the Russian Federation) is traditionally carried out by the results of the monitoring of relevant parameters of the disease in patient before treatment and after treatment measures. However, it is quite often that with multi-parameter monitoring of the patient is not all of the observed parameters xi may show substantial (in the framework of stochastic criteria) change of parameters of the entire state vector xi of the human body in the form x = x (t) = (x1, x2,..., xm)T where m is dimension of the phase space of states. In such cases there is an uncertainty of type 1 (sort) when the stochastics show low efficiency. Then it is supposed that there is a efficiency of treatment, otherwise there is a need to find other methods allowing more accurate measurement of real change of xi within the course of treatment. We show two ways of solving the problem of uncertainty of 1st type on the basis of the calculation of quasi-attractors of the state vector of the human body.
A new method of multivariate bioinformation analysis for studying of external respiratory function values in patients with bronchial asthma (BA) and concomitant type 2 diabetes mellitus (type 2 DM). System synthesis allows to define the most significant dynamic signs that can be changed while medical respiratory rehabilitation in patients with bronchial asthma and concomitant type 2 diabetes mellitus.
The basis of the third global paradigm of theory of chaos and self-organization, which focuses on the assessment of the chaotic dynamics of the state vector of complex biological systems using multi-dimensional phase space of states. The paper presents a comparative description of the effectiveness of the traditional stochastic methods and methods of calculating the parameters of quasi-attractors. It is showed the difference in efficiency (low) of stochastics, which leads to the uncertainty of the 1st kind, and methods of multidimensional phase spaces, providing the solution of system synthesis. Volumes quasi-attractors with kinesotherapy in patients with acute stroke increased 5.3 times in the initial stage of treatment, and then falling off sharply. It is discussed the need for parallel applications and stochastic methods and methods of theory of chaos and self-organization in the study of complex medical and biological systems.
Complex Biosystems (complexity) cannot be attributed to traditional chaotic systems, because for them it is impossible to calculate the autocorrelation function, Lyapunov exponent, no run properties of mixing, continuously the state vector x(t) demonstrates chaotic motion in the form άχίάίΦθ. Since the initial state x(to) is arbitrarily unrepeatable for such systems, type-one uncertainty and type-two uncertainty arise. Type-one uncertainty is characterized by absence of statistically significant differences between samples. The authors propose neurocomputing methods and theory of chaos and self-organization to differentiate these samples. The authors present examples of such a situation for the parameters of the cardio-respiratory system of humans in conditions of the latitudinal displacement of large groups of people. It is shown that the neuroemulator not only solves the problem of binary classification, but also identifies the order parameters in diagnostic signs. It is very important to increase the number of iterations in the repetition of binary classification. The number of iteration (when we repeat the neuroemulator procedure) has the fundamental role for identification of order parameters. Errors are possible within the order parameters with the high number of iterations.
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