电子病历里承载的大量医疗信息能够帮助医生更好地了解患者的情况，辅助医生进行临床诊断。作为中文电子病历信息提取的2大核心任务，命名实体识别和实体关系抽取的目标是识别出电子病历文本中的医学实体并提取出各个实体间的医学关系。首先，系统阐述了中文电子病历的研究现状，指出命名实体识别和实体关系抽取2大任务在中文电子病历信息提取中所发挥的重要作用。随后，介绍了面向中文电子病历信息提取的命名实体识别和关系抽取算法的最新研究成果，并分析了每个阶段各个模型的优缺点。最后，讨论了中文电子病历现阶段所存在的问题并对未来的研究趋势进行展望。


The large amount of medical information carried in the electronic medical record can help doctors better understand the situation of patients and assist doctors in clinical diagnosis. As the two core tasks of Chinese electronic medical record (EMR) information extraction, named entity recognition and entity relationship extraction have become the main research directions. Its main goal is to identify the medical entities in the EMR text and extract the medical relationships between the entities. This paper systematically expounds the research status of Chinese electronic medical record, points out the important role of named entity recognition and entity relationship extraction in Chinese electronic medical record information extraction, then introduces the latest research results of named entity recognition and relationship extraction algorithm for Chinese electronic medical record information extraction, and analyzes the advantages and disadvantages of each model in each stage. In addition, the current problems of Chinese EMR are discussed, and the future research trend is prospected.



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