Institute of Ethnology and Anthropology, Chinese Academy of Social Sciences, Beijing 100732, China.
Beijing Academy of Artificial Intelligence, Beijing 100084, China.
Math Biosci Eng. 2023 Feb 6;20(4):6776-6799. doi: 10.3934/mbe.2023292.
The knowledge graph is a critical resource for medical intelligence. The general medical knowledge graph tries to include all diseases and contains much medical knowledge. However, it is challenging to review all the triples manually. Therefore the quality of the knowledge graph can not support intelligence medical applications. Breast cancer is one of the highest incidences of cancer at present. It is urgent to improve the efficiency of breast cancer diagnosis and treatment through artificial intelligence technology and improve the postoperative health status of breast cancer patients. This paper proposes a framework to construct a breast cancer knowledge graph from heterogeneous data resources in response to this demand. Specifically, this paper extracts knowledge triple from clinical guidelines, medical encyclopedias and electronic medical records. Furthermore, the triples from different data resources are fused to build a breast cancer knowledge graph (BCKG). Experimental results demonstrate that BCKG can support knowledge-based question answering, breast cancer postoperative follow-up and healthcare, and improve the quality and efficiency of breast cancer diagnosis, treatment and management.
知识图谱是医疗智能的关键资源。通用医学知识图谱试图包含所有疾病，并包含大量医学知识。然而，手动审查所有三元组是具有挑战性的。因此，知识图谱的质量无法支持智能医疗应用。乳腺癌是目前癌症发病率最高的疾病之一。迫切需要通过人工智能技术提高乳腺癌的诊断和治疗效率，并改善乳腺癌患者的术后健康状况。针对这一需求，本文提出了一种从异构数据资源构建乳腺癌知识图谱的框架。具体来说，本文从临床指南、医学百科全书和电子病历中提取知识三元组。此外，来自不同数据资源的三元组被融合以构建乳腺癌知识图谱 (BCKG)。实验结果表明，BCKG 可以支持基于知识的问答、乳腺癌术后随访和医疗保健，并提高乳腺癌诊断、治疗和管理的质量和效率。
