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

doi:10.3778/j.issn.1673-9418.2101029

Abstract

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.

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

摘要：

近年来，随着互联网的普及和大数据分析等技术的发展，人们对移动医疗服务的需求越来越迫切，具体表现为根据症状确定自己患有的疾病以及根据疾病选择服务质量较好的医院及医生。为了解决上述问题，基于知识图谱和深度学习技术设计并实现了一种问诊推荐系统。基于互联网开放的医疗数据，构建了“疾病-症状”知识图谱，帮助用户根据症状自查，并以知识图谱嵌入模型训练知识图谱中实体的嵌入向量表示，根据向量的欧式距离相似度选取最相近的疾病实体丰富推荐选项，两者结合实现疾病诊断服务。同时，基于社交媒体的评论数据，结合现有的医疗服务质量评价指标，使用了深度学习的分析方法，自动给出医生的服务质量多维度的评分，为用户提供医生医院推荐服务。最后，通过构建测试集以及设计调查问卷等方式，验证了疾病诊断服务和医生医院推荐服务的准确率分别达到了74.00%和90.91%。

关键词: 医疗知识图谱, 评论情感分析, 深度学习, 推荐系统

WU Jiawei, SUN Yanchun. Recommendation System for Medical Consultation Integrating Knowledge Graph and Deep Learning Methods[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1432-1440.

武家伟, 孙艳春. 融合知识图谱和深度学习方法的问诊推荐系统[J]. 计算机科学与探索, 2021, 15(8): 1432-1440.

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