Aim. The aim of the study was to create a computer decision support system using expert knowledge for the diagnosis of rare hereditary diseases due to the difficulty of their identification at the pre-laboratory stage.Material and Methods. Descriptions of the clinical picture of lysosomal storage diseases from literature sources were used as the research material. The methods included knowledge extraction, expert assessments, quantization of age intervals, and applied intelligent services to form a knowledge base.Results. The results of the study include the construction of models for a complex assessment of a sign and an integral assessment of a disease, on the basis of which the comparative analysis algorithm is implemented to assess each of the hypotheses put forward by the system. The results of testing the prototype of the created expert system on a control sample of patients with mucopolysaccharidosis showed the efficiency of 90%. Discussion. In the discussion, several diagnostic systems are considered and their distinction from the system, presented in this work, is shown. Conclusion. The results of the development of intelligent system based on knowledge for the diagnosis of lysosomal storage diseases are summarized and the perspectives for its development are highlighted.
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Blagosklonov N. A., Kobrinskii B. A. Differential diagnosis of hereditary metabolic diseases using the expert knowledge-based system. The Siberian Journal of Clinical and Experimental Medicine. 2020;35(4):71-78.