摘要
针对中文电子病历命名实体识别任务中存在的边界划分不准确、实体识别率不高等问题,提出基于深度学习的CNN-BiLSTM-CRF模型,详细阐述模型结构与原理,采集3127份中文电子病历数据进行实验以验证模型性能,结果表明该模型具有较好的识别效果及性能。
Aiming at the problems of inaccurate boundary division and low entity recognition rate in the Named Entity Recognition(NER)task of Chinese Electronic Medical Records(EMR),the paper proposes a CNN-BiLSTM-CRF model based on deep learning,expounds the structure and principle of the model in detail,and collects 3127 Chinese EMR for experiments to verify the performance of the model.The results show that this model achieves better recognition effect and better performance.
作者
马欢欢
孔繁之
高建强
MA Huanhuan;KONG Fanzhi;GAO Jianqiang(School of Software,Qufu Normal University,Qufu 273100,China;School of Medical Information Engineering,Jining Medical University,Rizhao 276826,China)
出处
《医学信息学杂志》
CAS
2020年第4期24-29,共6页
Journal of Medical Informatics
基金
教育部产学合作协同育人项目“高精度人脸识别技术与教学平台建设研究”(项目编号:201801245011)。
关键词
中文电子病历
命名实体识别
卷积神经网络
Chinese Electronic Medical Records(EMR)
Named Entity Recognition(NER)
Convolutional Neural Network(CNN)
