Differential diagnosis of lysosomal storage diseases using the ontological knowledge base

The problem of early and timely accurate diagnosis of rare hereditary diseases is global. The use of physician-assisted computer systems could solve it. There are various foreign medical decision support systems. However, there are currently no domestic functioning systems to resolve these issues.
The aim of this study is to improve the timeliness and accuracy of making a correct diagnosis in patients with signs of hereditary lysosomal storage diseases using an intelligent computer decision support system.
Materials and methods. To fill the knowledge base, various sources of medical information containing descriptions of the phenotypic manifestations of a group of lysosomal storage diseases were analyzed. The knowledge extracted from the literature was supplemented by three expert assessments — the coefficient of modality, the confidence measures of manifestation and degree of expression. For clinical approbation of the system, 35 clinical cases from the literature and depersonalized extracts from the electronic health records of 75 patients treated at four specialized medical organizations of the Russian Federation were used.
Results. The GenDiES expert decision support system for the differential diagnosis of orphan hereditary diseases has been developed. The knowledge base of the system is implemented on the IACPaaS cloud platform in the form of an ontological network. This made it possible to describe diseases considering expert assessments for four selected age periods and entering data from patients with suspected hereditary diseases. The comparative analysis algorithm was used to assess the similarity of the patient’s clinical features with expert descriptions. The accuracy of the test results was 88.18% for the differential diagnostic series of five hypotheses.
Conclusion. Implementation of the knowledge base in the form of an ontological model provided the GenDiES expert system with high efficiency at the stage of forming hypotheses at the pre-laboratory stage of diagnosis.

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