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.
ObjectiveInterpret the interpretation of intervention description and reporting standards (TIDieR), and further present the domestic and international application status of TIDieR based on knowledge graphs. The aim is to provide references and inspirations for standardized reporting of intervention studies. MethodsProvide a detailed interpretation of TIDieR based on examples. Retrieve TIDieR-related literature published in Chinese and English databases such as CNKI, WanFang Data, PubMed, and Web of Science from 2014 to 2024, and conduct visual analysis using CiteSpace6.3.R1 bibliometric software. ResultsTIDieR consisted of 12 entries, including abbreviated intervention name, implementation rationale, implementation materials, implementation process, implementer, implementation method, implementation site, implementation time and intensity, personalized plan, plan changes, expected effects, and actual effects. The bibliometric analysis included 94 English-language papers and 5 Chinese-language papers. The application of TIDieR was relatively widespread overseas, mainly involving health care, rehabilitation, and digital health fields. ConclusionTIDieR can ensure the standardization and reproducibility of intervention research reports. However, domestic scholars still apply TIDieR less frequently. It is necessary to gradually promote and strengthen the application of TIDieR in future intervention studies, thereby improving the transparency and quality of intervention research reports.