부산대학교 도서관

In order to design braces that are more in line with patient characteristics, and help clinicians achieve rapid and accurate diagnosis and treatment. Starting from practical application, this paper crawls AIS brace-related knowledge from medical websites, combines electronic cases and expert knowledge, builds AIS brace knowledge graph, and summarizes the main knowledge of AIS. Due to the complexity of the knowledge of AIS braces, this paper proposes a joint entity and relation extraction method based on the FS-E-BIESO annotation method. By comparing the two knowledge extraction algorithms BERT-BiLSTM-CRF and BiLSTM-CRF, it is concluded that BiLSTM-CRF has a better F1 value. By comparing the two knowledge extraction algorithms BERT-BiLSTM-CRF and BiLSTM-CRF, it is concluded that BiLSTM-CRF has a better F1 value. The extracted knowledge is merged to eliminate the interference knowledge, and imported into neo4j in the form of triples to construct the knowledge graph of AIS orthopedic braces.