Abstract
Knowledge graph can effectively analyze and construct the essential characteristics of data. At present, scholars have proposed many knowledge graph models from different perspectives, especially in the medical field, but there are still relatively few studies on stroke diseases using medical knowledge graphs. Therefore, this paper will build a medical knowledge graph model for stroke. Firstly, a stroke disease dictionary and an ontology database are built through the international standard medical term sets and semiautomatic extraction-based crowdsourcing website data. Secondly, the external data are linked to the nodes of the existing knowledge graph via the entity similarity measures and the knowledge representation is performed by the knowledge graph embedded model. Thirdly, the structure of the established knowledge graph is modified continuously through iterative updating. Finally, in the experimental part, the proposed stroke medical knowledge graph is applied to the real stroke data and the performance of the proposed knowledge graph approach on the series of Trans ∗ models is compared.
Highlights
With the acceleration of urbanization and social aging, stroke has become one of the diseases with a high rate of death and disability in our country
Since this paper proposes to formulate a stroke disease dictionary, the entity alignment step has been completed in the data extraction stage, so the entity attribute alignment stage is mainly to align the attributes
Stroke is a disease that urgently needs to reduce the risk of treatment. e proposed stroke-oriented medical knowledge graph can effectively discover the associations between medical entities and establish a certain foundation for subsequent intelligent question-and-answer and medical assistance decision-making systems
Summary
With the acceleration of urbanization and social aging, stroke has become one of the diseases with a high rate of death and disability in our country. Stroke is an acute cerebrovascular disease that causes brain tissue loss due to blockage or sudden rupture of blood vessels in the brain It has the characteristics of high morbidity, mortality, disability, and recurrence. It is impossible to directly use traditional technological means to effectively analyze the medical entity relationship of stroke, which brings a lot of inconvenience to the further prevention and treatment of stroke. We can apply the knowledge graph to intelligent question answering, disease-assisted diagnosis, risk assessment, and decision-making systems in the medical field and perform information screening and representation on the information of medical entities thereby establishing a database of medical knowledge relations. Erefore, it is very necessary to design a reasonable medical knowledge graph model for stroke, to dig the physical associations related to stroke, and provide a powerful strategy for effective prevention and treatment of stroke further.
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