LAUSR

The clinical sub-speciality departments are increasing. It is usually difficult for patients without medical education to choose a suitable department when they need to make an online registration with doctor. A novel approach is presented for departments recommendation in this paper. The ICD codes and symptoms of medical health data are used to describe the patients’ characteristics, diseases, and departments in the quantitative relation. The knowledge graph is built with the relations to automatically recommend departments. The MacBERT-BiLSTM-CRF medical entity recognition model (MacNER) is proposed to identify the patient’s symptoms, parts, treatment methods and drug entities to structure a knowledge graph. The knowledge graph is used to provide knowledge for the intelligent department recommendation. The identified entities are mapped to nodes in the graph with the entity mapping method. Finally, an algorithm named Feature Rate Multiply Converse Disease Rate (FRMCDR) is proposed. The most appropriate department can be recommended by fusing the patient’s chief complaint and past medical history. The experiment shows that our method obtained an 88.77% precision rate in the recommendation.