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Journal: Scientific Journal of Astana IT University (Vol.6, No. 6)

Publication Date:

Authors : ;

Page : 23-37

Keywords : information technology; medical information system; making decisions; algorithm; strategy; health care system.;

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The relevance of the topic is that currently modern medical information systems are aimed at providing management, economic and in some cases medical practice in the collection and processing of anamnestic data, including dental, ophthalmological, radiological, anesthesiological, resuscitation. This study goal is to develop models of basic principles and structural and functional scheme of the decision support system as a tool that allows to model the process of “clinical thinking” of an endocrinologist in determining plans for drug treatment of diabetes based on a scenario approach and decision theory. In accordance with the set goal, the research tasks are formulated and solved, the essence of which is as follows:- analyze existing medical decision support systems and identify shortcomings of such systems; - creating the criteria for developing medical decision support systems to improve usability; - develop acceptable algorithms to create a medical decision support system. Recommendations for modeling the functions of the doctor's intelligence in the process of processing and evaluation of medical information using a personal computer are offered, which will improve the operation of existing medical information systems. The research was conducted within the framework of project management methodology and decision theory using information technology tools. The algorithms for the creation a medical decision support system proposed by the authors are based on the method of multicriteria ranking of alternatives, which is a tool for modeling “clinical thinking” and logical reasoning of an endocrinologist. Unlike existing medical information systems, it will not only collect, store and process information about patients, but also increase the efficiency of endocrinologist decisions regarding the prediction and development of a patient treatment plan.

Last modified: 2021-11-30 00:52:51