The use of tools for automatic proof of mathematical logic theorems for modeling the dynamic balance of minerals in the human body

Abstract

Computer scientists build a logical framework for a particular subject or phenomenon. This methodology involves the use of axioms, which are self-evident statements that serve as the basis for a logical system. Axiomatic modelling begins by identifying the relevant concepts and objects that will be studied and then defining these concepts using axioms. These axioms are chosen based on their simplicity, consistency, and logical power, and are used to build a series of logical statements or theorems that describe the behaviour of the system being studied. This method allows researchers to build a clear and logical foundation for their work and allows them to prove the validity of their results through logical deduction. It is often used in mathematics and computer science, but can also be applied to other fields such as economics, physics, social science, biology and medicine. We proposed an extension of the method by incorporating knowledge about physiological processes in the human body by introducing biomedical parameters and logic and developed the novel method of Logical Dialectical Modelling (LDM). This original methodology uses, as tools, the logic of predicates of the 1st order and the Robinson method of automatic theorem proving. It prevents errors and simplifies the process of proving statements. The first time we applied LDM for the problem of providing the human body with the necessary dynamic balance of minerals. We analysed data [1,44,45] on the dependence of disease symptoms on the values of quantitative indicators of the concentration of minerals in the hair of children in the Chornobyl zone,LDM can provide a structured, logical approach to diagnostics that can help identify the root causes of problems and guide more effective treatment planning. LDM can be used together with artificial intelligence (AI) systems to improve the accuracy and efficiency of diagnostic processes. LDM is based on logical statements, they can be tested and refined using a rigorous, mathematical approach, which can help to increase the reliability and accuracy of the models. This can be useful for doctors in evaluating patients and making accurate diagnoses promptly. The purpose of the article is to demonstrate the use of LDM through in medicine too.