COMPUTATIONAL INTELLIGENCE METHODS TO PATIENTS STRATIFICATION IN THE MEDICAL MONITORING SYSTEMS
Keywords:Information Technology Data Stream Mining, Medical Monitoring Systems, Machine Learning Methods, Mathematical Models and Methods for Patient Stratification
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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Copyright (c) 2023 N. S. Bakumenko, V. Y. Strilets, M. L. Ugryumov, R. O. Zelenskyi, K. M. Ugryumova, V. P. Starenkiy, S. V. Artiukh, A. M. Nasonova
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