Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (8): 1432-1440.DOI: 10.3778/j.issn.1673-9418.2101029

• Science Researches • Previous Articles     Next Articles

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

WU Jiawei, SUN Yanchun   

  1. 1. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
    2. Key Lab of High Confidence Software Technologies, Ministry of Education, Peking University, Beijing 100871, China
    3. Peking University Information Technology Institute (Tianjin Binhai), Tianjin 300450, China
  • Online:2021-08-01 Published:2021-08-02



  1. 1. 北京大学 信息科学技术学院,北京 100871
    2. 北京大学 高可信软件技术教育部重点实验室,北京 100871
    3. 北京大学(天津滨海)新一代信息技术研究院,天津 300450


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.

Key words: medical knowledge graph, comment sentiment analysis, deep learning, recommendation system



关键词: 医疗知识图谱, 评论情感分析, 深度学习, 推荐系统