融合文字与标签的电子病历命名实体识别  

Named Entity Recognition of Electronic Medical Records Based on Texts and Labels

作　　者：赵奎[1,2] 杜昕娉 高延军 马慧敏[4] ZHAO Kui;DU Xin-Ping;GAO Yan-Jun;MA Hui-Min(Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;University of Chinese Academy of Sciences,Beijing 100049,China;The Fourth Affiliated Hospital of China Medical University,Shenyang 110032,China;Medical Solutions Business Division,Neusoft Group Co.Ltd.,Shenyang 110003,China)

机构地区：[1]中国科学院沈阳计算技术研究所,沈阳110168 [2]中国科学院大学,北京100049 [3]中国医科大学附属第四医院,沈阳110032 [4]东软集团股份有限公司医疗解决方案事业本部,沈阳110003

出　　处：《计算机系统应用》2022年第10期375-381,共7页Computer Systems & Applications

基　　金：国家水体污染控制与治理科技重大专项(2018ZX07601001)

摘　　要：准确的命名实体识别是结构化电子病历的基础,对于电子病历规范化编写有着重要的作用,而现今的分词工具对于专业的医疗术语无法做到完全正确的区分,使得结构化电子病历难以实现.针对医疗实体识别中出现的问题,本文提出了一种在命名实体识别领域中改进的BiLSTM-CRF深度学习模型.模型将文字和标签结合作为输入,在多头注意力机制中使模型关注更多的有用信息,BiLSTM对输入进行特征提取,得到每个文字在所有标签上的概率,CRF在训练过程中学习到数据集中的约束,进行解码时可以提高结果的准确率.实验使用人工标注的1000份电子病历作为数据集,使用BIO标注方式.从测试集的结果来看,相对于传统的BiLSTM-CRF模型,该模型在实体类别上的F1值提升了3%–11%,验证了该模型在医疗命名实体识别中的有效性.Accurate named entity recognition is the basis of structured electronic medical records and plays an important role in the standardized writing of electronic medical records.However,current word segmentation tools cannot completely and correctly distinguish professional medical terms,making it difficult to achieve structured electronic medical records.As for problems in medical entity recognition,this study proposes an improved deep learning model based on BiLSTM-CRF in the field of named entity recognition.The model combines text and labels as input,which makes the model focus on more useful information in the multi-head attention mechanism.BiLSTM performs feature extraction on the input and obtains the probability of each text on all labels.CRF learns the constraints of the data set during the training and improves the accuracy of the results after decoding.The experiment uses 1000 manually labeled electronic copies as the data set and the BIO for labeling.Compared with the traditional BiLSTM-CRF model,the proposed model raises the F1 value in the entity category by 3%–11%,verifying its effectiveness in named entity recognition of medical records.

关 键 词：结构化电子病历 命名实体识别 BiLSTM CRF 深度学习 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] R-05[自动化与计算机技术—计算机科学与技术]