Wang Caiyun, Zheng Zengliang, Cai Xiaoqiong, Huang Jihan, Su Qianmin
College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China.
Center for Drug Clinical Research, Shanghai University of Chinese Medicine, shanghai 201203, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):1040-1044. doi: 10.7507/1001-5515.202204016.
With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
随着医学信息技术和计算机科学的蓬勃发展，医疗服务行业正逐步从信息化向智能化转变。医学知识图谱在知识问答、智能诊断等智能医疗应用中发挥着重要作用，是推动智慧医疗的关键技术和医疗信息智能管理的基础。为了充分挖掘知识图谱在医学领域的巨大潜力，本文重点从药物相互作用关系发现、辅助诊断、个性化推荐、决策支持和智能预测五个方面展开研究。介绍了医学知识图谱的最新研究进展，并针对医学知识图谱目前面临的挑战和问题提出了相关建议，以期为推动医学知识图谱的广泛应用提供参考。
