A Multi-Dimensional Context-Aware Healthcare Service Recommendation Method

Due to the outbreak of COVID-19, online diagnosis and treatment services have developed rapidly, but it is not easy for patients to choose the appropriate healthcare service in the face of massive amounts of information. This article proposes a multi-dimensional context-aware healthcare service recommendation method, which consists of a healthcare service matching model and a healthcare service ranking model. The former first collects objective knowledge related to doctors and diseases to build a knowledge graph, then matches a group of healthcare services for patients according to the patient's input; the latter selects five indicators from the doctor's academic level, geographical location, public influence, reputation, etc. to build a TOPSIS model based on the entropy weight method to recommend the most appropriate healthcare services for patients. Finally, taking the patient in Shiyan as an example, the whole process of the method is demonstrated, and the feasibility of the method is verified.