Intelligent Data Analysis As an Evidence-Based Medicine Tool

Some possible mathematical models and methods of intelligent data analysis (IDA) for the field of evidence-based medicine (EBM) are discussed. Two critically significant limitations for the application of traditional (statistics-based) EBM approach are considered: work with open subject areas and with small (statistically nonsignificant) collections of analyzed data. A special class of IDA methods based on the computer-oriented formalization of causal similarity heuristics using logical and algebraic means is presented. Options for clarifying the concept of evidence-based are proposed, which allow the indicated limitations of the traditional EBM approach to be circumvented. Some practically significant characteristics of this variant of the use of artificial intelligence methods in the tasks of evidence-based medicine are discussed.