Methodology of Constructing Statistical Models for Nonlinear Non-stationary Processes in Medical Diagnostic Systems

The article presents a methodology for analysis and modeling nonlinear and non-stationary processes associated with medical diagnostics and solving medical problems. The methodology is based on collecting and preliminary processing of statistical data, identifying and accounting for possible uncertainties, building and estimating the structure of mathematical model, and evaluating its parameters, estimating model based forecasts and calculating statistical criteria for the model adequacy as well as quality of the forecasts. The analysis of selected models for linear and nonlinear processes is presented. A scheme for combining forecasts when estimating a diagnosis is proposed. An example of using a diagnostic system for predicting a patient's condition using combined (linear + nonlinear) model is given and the methods used are analyzed.