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Medical data processing is one of the priority machine learning areas. Usually, data obtained in the process of medical patient monitoring are complex and have a different nature. Solving the problem of clustering, classification, or forecasting problem these data requires the creation of new methods or improvement of existing methods to improve the decision accuracy and effectiveness. The classical clustering approaches and the c-means fuzzy clustering method were analyzed. Based on the multiagent systems theory, it is proposed to use in the c-means method the separate rules for selecting elites when forming clusters and selecting the best of them in accordance with the chosen intra-cluster distance measures. The result of solving such a problem is the number of clusters, as well as the number of elements in them. The method quality was tested on Fisher iris data set using three measures of intra-cluster distance: Mahalanobis distance, Mahalanobis distance considering the membership function, and Kullbak-Leibler entropy. The highest accuracy of 98% was obtained for the distance measured by the Kullbak-Leibler entropy. Therefore, this measure was chosen to solve the clustering problem of medical monitoring data for prostate disease. Medical monitoring data were divided into four classes of patient states: “healthy persons”, “non-metastatic patients”, “metastatic patients” and “hormone-resistant patients”. The accuracy of clustering according to medical data was 95,6%. In addition to accuracy, the confusion matrix, ROC- and LF-curves were used to assess the method quality. The minimum value of the ROC-curve was 0.96 for Fisher's irises and 0.95 for medical monitoring data, which characterizes the high quality of the proposed clustering method. The loss function value is also quite small (-0.056 and -0.0176 for each considered data set), which means that the optimal cluster number and the distribution of data over them are obtained. Based on the obtained results analysis, the proposed method can be recommended for use in medical information and diagnostic decision support systems for clustering monitoring data.

Context. In modern medical practice the automation and information technologies are increasingly being implemented for diagnosing diseases, monitoring the condition of patients, determining the treatment program, etc. Therefore, the development of new and improvement of existing methods of the patient stratification in the medical monitoring systems is timely and necessary. Objective. The goal of intelligent diagnostics of patient’s state in the medical monitoring systems – reducing the likelihood of adverse states based on the choice of an individual treatment program: − reducing the probability of incorrectly determining the state of the patients when monitoring patients; − obtaining stable effective estimates of unknown values of treatment actions for patients (corresponding to the found state); − the choice of a rational individual treatment program for the patients, identified on the basis of the forecasted state. Method. Proposed methodology, which includes the following computational intelligence methods to patient’s stratification in the medical monitoring systems: 1) method of cluster analysis based on the agent-based approach – the determination of the possible number of patient’s states using controlled variables of state; 2) method of robust metamodels development by means artificial neuron networks under a priori data uncertainty (only accuracy of measurements is known) in the monitoring data: a) a multidimensional logistic regression model in the form of analytical dependences of the posterior probabilities of different states of the patients on the control and controlled variables of state; b) a multidimensional diagnostic model in the form of analytical dependences of the objective functions (quality criteria of the patient’s state) on the control and controlled variables of state; 3) method of estimating informativeness controlled variables of state at a priori data uncertainty; 4) method of robust multidimensional models development for the patient’s state control under a priori data uncertainty in the monitoring data in the form of analytical dependencies predicted from the measured values of the control and controlled variables of state in the monitoring process; 5) method of reducing the controlled state variables space dimension based on the analysis of the variables informativeness of the robust multidimensional models for the patient’s state control; 6) method of patient’s states determination based on the classification problem solution with the values of the control and forecasted controlled variables of state with using the probabilistic neural networks; 7) method of synthesis the rational individual patient’s treatment program in the medical monitoring system, for the state identified on the basis of the forecast. Proposed the structure of the model for choosing the rational individual patient’s treatment program based on IT Data Stream Mining, which implements the «Big Data for Better Outcomes» concept. Results. The developed advanced computational intelligence methods for forecast states were used in choosing the tactics of treating patients, to forecast treatment complications and assess the patient’s curability before and during special treatment. Conclusions. Experience in the implementation of “Big Data for Better Outcomes” concept for the solution of the problem of computational models for new patient stratification strategies is presented. Advanced methodology, computational methods for a patient stratification in the medical monitoring systems and applied information technology realizing them have been developed. The developed methods for forecast states can be used in choosing the tactics of treating patients, to forecast treatment complications and assess the patient’s curability before and during special treatment.

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The approach of system improvement, methodology and information technology of turbofan engine elements main parameters and operating tolerances robust estimation on basis of inverse problems concept by stochastic optimization problem are proposed. The method of multicriterion system modification problem quasisolution searching with input data uncertainly and with bordering of feasible solution class is offered. Quasisolution synthesis is realized by regularization of smoothing functional minimum searching with using the A.N. Tihonov’s method. The multicriterion modification problem numerical solution searching evolution method was created, it based on genetic algorithm using. On basis of development methodology the system aerodynamic turbofan engine (TFE) compressor improvement were made.