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Mathematical Methods for Structuring and Formalization of Medical Experience
Author(s) -
I.V. Barinova,
V. M. Guryeva,
Yuri Kotov,
Tatiana Semenova
Publication year - 2018
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2018.11.014
Subject(s) - computer science , structuring , field (mathematics) , process (computing) , representation (politics) , knowledge representation and reasoning , logical analysis , management science , artificial intelligence , data science , mathematical statistics , statistics , mathematics , finance , politics , political science , pure mathematics , law , economics , operating system
The article is devoted to the study of medical knowledge structure. In the process of the patient’s treatment, the doctor makes a number of important decisions, not all of which are within the theoretical knowledge obtained during training at the University or from professional literature. His personal experience gives much. But practical experience is usually scattered and non-systematic. The doctor produces himself the ordering and justification of the information. Our data processing methods allow us to systematize the new knowledge elements and to select suitable reasons for diagnostic rules formulating. For this purpose we use: semi-quantitative models of the observed processes (sliding standards), three-valued logic for analysis of concepts interrelations, representation of knowledge elements as the logical symptoms, formulating rules as "masks" for the logical symptoms vectors. These methods form a concept system insensitive to gaps in data and allow comparison of patient’s groups whose descriptions coincide only partially. The rules working out with procedure based on these methods suppose the joint work of physicians and mathematicians team. Practical examples are related to the field of obstetric diagnosis and forecasting problems.

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