The way of inductive formation of medical diagnostic knowledge bases

Abstract

The paper provides an introduction into the area of inductive formation of knowledge bases. It presents traditional definitions of main problems in this area and highlights the current topical questions including the interpretability of the results. For solving of current problems in defined area the method of inductive formation of easily interpretable medical diagnostic knowledge bases is suggested. It includes the new definitions of classification and clustering problems for dependence models with parameters and the learning algorithm (solving mentioned problems in their new definitions) developed for the practically useful and easily interpretable mathematical dependence model with parameters which is a near real-life ontology of medical diagnostics (defined by a system of logical relationships with parameters). Also it includes the software package InForMedKB (INductive FORmation of MEDical Knowledge Bases) which implements above mentioned learning algorithm. InForMedKB allows to create training sets (consisting of clinical histories from various therapeutic areas) and to use them for inductive formation of medical diagnostic knowledge bases. These knowledge bases are presented in form accepted in the medical literature and contain descriptions of diseases (from specified therapeutic areas) as well as an explanation of these knowledge bases based on descriptions of clinical histories from used training sets. The formal representation of medical knowledge bases enables their usage for intelligent systems for medical diagnostics.