On the Problem of Medical Diagnostic Evidence: Intelligent Analysis of Empirical Data on Patients in Samples of Limited Size

This paper discusses the possibility of expanding the ideas of the validity of medical decisions of a diagnostic nature, which are made in the framework of so-called evidence-based medicine. An approach is proposed that allows building special data in the process of intelligent analysis of accumulated empirical data, which characterize the causality of a diagnosed effect–logical conditions (characteristic functions) that take the value true in all instances of the presence of the target effect and the value false for all instances of its absence in the training sample of precedents. This problem is solved based on the expanding sequences of training samples using: (a) a formal refinement of the concept of similarity of precedent descriptions as a binary algebraic operation, and (b) a mathematical technique for generating empirical dependences in the style of the JSM method of automated support for scientific research. The features and capabilities of the developed approach are described based on the example of solving the problem of analyzing the causes and predicting the pseudoprogression of brain tumors.