В работе описаны принципы создания компьютерной консультативной системы поддержки принятия решений при диагностике наследственных заболеваний на долабораторном этапе. Прототип реализуется на примере клинических проявлений лизосомных болезней накопления. В основу системы положены литературные данные, которые сопровождаются оценками экспертов. Разрабатываемая интеллектуальная система формирует ограниченный дифференциально-диагностический ряд гипотез. Это обеспечивает помощь врачу-генетику в дифференциальной диагностике как при наличии у него предварительного диагноза, так и при отсутствии у него предположений о нозологии заболевания.
The paper describes the principles of creating a computer consultative decision support system for the diagnosis of hereditary diseases at the pre-laboratory stage. The prototype is implemented on the example of the clinical manifestations of lysosomal storage diseases. The system is based on literary data, which are accompanied by expert assessments. The developed intellectual system forms a limited differential diagnostic row of hypotheses. It provides assistance to the geneticist in differential diagnosis both in the presence of a preliminary diagnosis and in the absence of assumptions about the nosology of the disease.
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.
П о данным Национальной ассоциации США, 3% новорожденных детей впоследствии имеют умственную отсталость, вызванную разными причинами и в значительной части случаев генетическими. Примерно в 20% случаев умственная отсталость обусловлена моногенными заболеваниями с различным характером наследования [1,2]. В то же время основная причина умственной отсталости остается неизвестной почти у 80% пациентов [3].Примером заболеваний с нарушением умственного развития служат лизосомные болезни накопления, характеризующиеся ранней задержкой психо-
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