MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records - Zhao, Genghong; Gu, Wenjian; Cai, Wei; Zhao, Zhiying; Zhang, Xia; Liu, Jiren (Frontiers in Genetics, 2022)
As a typical knowledge-intensive industry, the medical industry is well suited to the use of knowledge graph technology for constructing causal inference calculations such as "symptom-disease", "laboratory examination/imaging examination-disease", and "disease-treatment method". Increasingly large electronic clinical records provide an opportunity for learning medical knowledge through machine learning. In this process, the issues of how to extract entities with a medical logic structure and how to bring entity extraction more in line with the logic of the text content in electronic clinical records have become key to building high-quality medical knowledge graphs. In this work, we describe a method for extracting medical entities using real Chinese electronic clinical records. We define a computational architecture called MLEE(Multi-Layers Entity Extraction) to extract object-level entities with "object-attribute" dependencies. Finally, we conduct experiments on the electronic clinical records of 1000 patients randomly selected from Shengjing Hospital of China Medical University to verify the effectiveness of the method.