Recommendation System for Medical Consultation Integrating Knowledge Graph and Deep Learning Methods

Abstract

In recent years, with the popularization of Internet and technologies like big data analysis, the demand for mobile medical services has become more and more urgent, which mainly focuses on ascertaining their diseases based on symptoms and further choosing hospitals and doctors with good service quality based on diseases. In order to tackle problems above, this paper designs and implements a recommendation system for medical consultation based on knowledge graph and deep learning. Using the open data on Internet, a “disease-symptom” knowledge graph is constructed. Once given symptom description, a disease candidate set is built to help user self-diagnose. To improve the accuracy of disease diagnosis, a vector representation of entities in the knowledge graph is trained by a knowledge graph embedding model. Then the disease candidate set is expanded by selecting disease entity with the shortest Euclidean distance with diseases in the set. Combining the two above, disease diagnosis service is provided. To recommend hospitals and doctors, given open media data, this paper uses a deep learning model and combines it with existing quality evaluation indicators for medical service to achieve scoring for doctors multi-dimensional service quality automatically. Finally, this paper verifies the accuracy of the disease diagnosis service and the doctor recommendation service by constructing test sets and designing questionnaires, which reach 74.00% and 90.91%, respectively.

Keywords

• medical knowledge graph
• comment sentiment analysis
• deep learning
• recommendation system