Affiliation:
1. Institute of Ethnology and Anthropology, Chinese Academy of Social Sciences, Beijing 100732, China
2. Beijing Academy of Artificial Intelligence, Beijing 100084, China
Abstract
<abstract><p>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.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
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