Diagnosis of orphan (rare), in particular hereditary diseases, is associated with difficulties due to the diversity of pathology and polymorphism (multi-variance) of phenotypic manifestations. This is the source for frequent errors at the stage of primary diagnosis. In this regard, there is a need to improve the accuracy of differential diagnosis at the pre-laboratory stage. To support medical decisions, it is advisable to use intelligent systems with developed knowledge bases. The rarity of hereditary diseases is the basis for the use of expert knowledge. In the system of hereditary lysosomal storage diseases, knowledge extraction was two-stage. At the first stage, knowledge about the clinical manifestations of diseases was extracted from literary sources. At the second stage, expert determined the confidence measure in various attributes (characteristics) of signs. The knowledge base is implemented since a fuzzy disease model that uses ontologies to integrate diverse information and includes a comprehensive expert assessment of signs (modality, manifestation, degree of expression). Based on the comparative analysis algorithm, the new case is compared with the reference variants of the integral model corresponding to diagnostic hypotheses, including their subsequent ranking. The explanation block allows to present the data that served as the basis for the hypothesis based on signs confirming the hypothesis put forward, missing to confirm the hypothesis, or not related to the diagnosis. The expert system is implemented in the ontological environment of a specialized cloud platform. The results of clinical testing of the system showed a high (above 85%) efficiency of differential diagnosis.
Boris A. Kobrinskii, Nikolay A. Blagosklonov, Valeriya V. Gribova, Elena A. Shalfeeva. Expert System for the Diagnosis of Orphan Diseases // Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). IITI 2022. Lecture Notes in Networks and Systems, vol 566. Springer, Cham. 2023.