[Sequential Bayes' strategy and mechanism of decision making in the OMIS software]

The paper describes the method of image recognition in which a useful element of Bayes' approach, numerous alternatives, is united with the productive idea of heterogeneous sequential analysis--ordering of signs by their informative value for decision making. The idea of the method--a sequential Bayes' algorithm--consists in the following: the a priori probabilities are not determined before-hand but specified in turn, depending on the empirical material. The author proves that algorithms conforming to the Neumann-Pierson or Wald strategies, specifically, the heterogeneous sequential algorithm, are not to be referred to the Bayes' algorithms, as is usually done in analyses of clinical laboratory data. Ideologically the heterogeneous sequential algorithm was developed as a method for analysis of empirical data, whereas the Bayes' approach is a deductive method. Confusion of the Bayes' approach with the algorithms based on the probability ratios is methodologically unjustified at least because the Neumann-Pierson and Wald's approaches are the greatest statistical achievements of the twentieth century and have nothing to do with the Bayes' formula. The methodology of constructing the probability measure in the clinical laboratory signs space is described in detail, as are the new objects, interval and binary structures, which emerge in the course of this construction. These objects help improve the diagnostic significance of clinical laboratory information even in cases when the results of analyses are apparently normal. The sequential Bayes algorithm is compared with the traditional Bayes approach to certain clinical problems. The author concludes that the sequential Bayes algorithm is a serious alternative to algorithms for making multiple-alternative decisions in the solution of clinical tasks.