Knowledge graph construction of patient relationship based on epidemiological investigation data

As the number of patients infected with the novel coronavirus increases, a large amount of epidemiological investigation data associated with them has been generated. Based on the data, the semantic association features among patients can be analyzed to express the disease transmission process at the individual level and to explore the distribution of patient characteristics and the transmission paths among patients. Firstly, the semantic relationship of patients is defined based on the analysis of flow modulation data, and the pattern layer of the patient relationship graph is designed accordingly. Then, the data layer is constructed by identifying patients and place entities and extracting "patient-relation-patient" and "patient-residence-place" triplets. Finally, the Neo4j graph database is used to visualize and analyze the patient relationship graph. The results show that the patient relationship graph can explore the intrinsic association of patients, effectively integrate the semantic relationship of patients, and express the process of disease transmission among patients by verifying the super spreader analysis and route of transmission.