基于电子病历的实体识别和知识图谱构建的研究

针对中文电子病历中命名实体识别和实体关系抽取研究方法中存在的问题，提出了一种基于双向长短时记忆网络（bidirectional long short term memory）与CRF（conditional random field）结合的实体识别和实体关系抽取方法。该方法首先使用词嵌入技术将文本转换为数值向量，作为神经网络BiLSTM的输入，再结合CRF链式结构进行序列标注，输出最大概率序列，并对识别结果知识图谱化。实验证明，该方法对中文电子病历进行实体识别和实体关系抽取时的准确率、召回率、◢F◣值有明显的提升。实验结果满足临床中系统应用需求，对帮助研究构建临床决策支持系统、个性化医疗推荐服务有引导作用。

Research on entity recognition and knowledge graph construction based on electronic medical records

Aiming at the problems in the research methods of named entity recognition and entity relationship extraction in Chinese electronic medical records), this paper proposed an entity identification and entity relationship based on bidirectional long short term memory and conditional random field(CRF). The method first used word embedding technology to convert text into numerical vector, as the input of neural network BiLSTM, combined with CRF chain structure for sequence labeling, output the maximum probability sequence, and mapping the recognition result knowledge graph by using the database tool Neo4j. Experiments show that the method can significantly improve the accuracy, recall rate and F value of entity identification and entity relationship extraction in Chinese electronic medical records. The experimental results meet the needs of clinical system applications, and have a guiding role in helping to study and construct clinical decision support systems and personalized medical recommendation services.