Construction of general medical knowledge graph based on evidence-based medicine and electronic medical record data

Corresponding author: hekunlun, kunlunhe@plagh.org

DOI: 10.12201/bmr.202409.00027

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  Abstract: Purpose/Significance To construct a general medical knowledge graph covering evidence-based medical knowledge and electronic medical record (EMR) data to support the application of data governance, clinical assisted decision making, treatment plan recommendation and other applications, and improve the application effect of the graph with the help of real-world expert experience. Method/Process The multi-source heterogeneous data was sorted out, the well-known knowledge graphs at home and abroad were integrated, the schema of the graphs was designed, and the word embedding of RoBERTa pre-trained model was used to identify named entities and extract relationships from medical literature, network literature, textbooks, medical databases and electronic medical records. The rule-based SWIQA framework and the manual audit strategy based on random sampling were used to evaluate the quality of the map. Result/Conclusion A total of 128 ontologies and 1108 relationships were identified and stored in the database in the form of triples. The semantic accuracy of the atlas was 93.8%. The general knowledge graph constructed by the study not only covers evidence-based medical knowledge, but also includes expert experience generated in the clinical real world, which can provide support for medical AI applications.

  Key words: Evidence-based medicine, EMR, Medical knowledge graph; ; 

  Submit time: 14 September 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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