Department of Cardiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, Guangxi, People's Republic of China.
Jinan University, Guangzhou, 510632, Guangdong, People's Republic of China.
Adv Ther. 2022 Sep;39(9):4052-4060. doi: 10.1007/s12325-022-02254-7. Epub 2022 Jul 30.
A knowledge graph is defined as a collection of interlinked descriptions of concepts, relationships, entities and events. Medical knowledge graphs have been the most recent advances in technology, therapy and medicine. Nowadays, a number of specific uses and applications rely on knowledge graphs. The application of the knowledge graph, another form of artificial intelligence (AI) in cardiology and cardiovascular medicine, is a new concept, and only a few studies have been carried out on this particular aspect. In this brief literature review, the use and importance of disease-specific knowledge graphs in exploring various aspects of Kawasaki disease were described. A vision of individualized knowledge graphs (iKGs) in cardiovascular medicine was also discussed. Such iKGs would be based on a modern informatics platform of exchange and inquiry that could comprehensively integrate biologic knowledge with medical histories and health outcomes of individual patients. This could transform how clinicians and scientists discover, communicate and apply new knowledge. In addition, we also described how a study based on the comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease used a knowledge graph to show the dietary factors associated with cardiovascular diseases in Nurses' Health Study data. To conclude, in this fast-developing world, medical knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. They could be a major and effective tool in cardiology and cardiovascular medicine and play an important role in reaching effective clinical decisions during treatment and management of patients in the cardiology department.
知识图谱被定义为概念、关系、实体和事件的相互关联描述的集合。医学知识图谱是技术、疗法和医学方面的最新进展。如今,许多特定用途和应用都依赖于知识图谱。知识图谱作为人工智能(AI)在心脏病学和心血管医学中的另一种形式的应用是一个新概念,只有少数研究涉及这一特定方面。在这篇简短的文献综述中,描述了疾病特异性知识图谱在探索川崎病各个方面的用途和重要性。还讨论了心血管医学中个体化知识图谱(iKGs)的设想。这种 iKG 将基于一个现代信息学平台,用于交换和查询,可以全面整合生物学知识以及个体患者的病史和健康结果。这将改变临床医生和科学家发现、交流和应用新知识的方式。此外,我们还描述了一项基于对急性心肌梗死和致命性冠心病相关饮食因素的综合纵向评估的研究,该研究如何使用知识图谱来展示护士健康研究数据中与心血管疾病相关的饮食因素。总之,在这个快速发展的世界中,医学知识图谱已成为有吸引力的数据存储和假设生成方法。它们可能是心脏病学和心血管医学中的主要有效工具,并在心脏病学治疗和管理患者的治疗和管理中发挥重要作用,以做出有效的临床决策。