Rare diseases have problems with low number of cases, low social awareness, and long time of diagnosis. “Targeted doctor” is the first step to help rare disease patients start the correct path of diagnosis and treatment. This article introduces the design of a decision-making engine for patients with rare diseases by constructing a knowledge graph of rare diseases and experts, using an intelligent question-and-answer system, and combining big data and artificial intelligence methods. This engine can perform rare disease pre-screening based on patient portraits and other information, and recommend the best visiting route to patients, thereby improving the efficiency of rare disease patients’ medical service system and enhancing the decision-making ability of rare diseases.
ObjectiveTo conduct a scoping review of studies on the application of knowledge mapping in the field of rare diseases at home and abroad, in order to clarify the content and status of application and provide references for future research in this field. MethodsRelevant studies in PubMed, Web of Science, Embase, MEDLINE, CNKI, WanFang Data, VIP, and CBM databases were searched, using the Joanna Briggs Institute Scoping Review Guidelines in Australia as the methodological framework, and the search time frame was from the establishment of the database to June 1, 2023. ResultsTwenty-five papers were included, and the main applications of knowledge graphs in the field of rare diseases were knowledge management, assisted diagnosis, drug repositioning and decision support, involving techniques such as knowledge representation, knowledge extraction, knowledge reasoning, knowledge fusion and knowledge storage.ConclusionKnowledge graphs have shown positive results in fusing and exploiting multi-source information, aiding disease prediction and diagnosis and drug development, but further technical improvements are needed.