[1]张明淘,韩 普.医疗实体识别研究进展[J].计算机技术与发展,2020,30(04):57-62.[doi:10. 3969 / j. issn. 1673-629X. 2020. 04. 011]
　ZHANG Ming-tao,HAN Pu.Research Progress on Medical Entity Recognition[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(04):57-62.[doi:10. 3969 / j. issn. 1673-629X. 2020. 04. 011]
点击复制

医疗实体识别研究进展()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年04期
页码:
57-62
栏目:
智能、算法、系统工程
出版日期:
2020-04-10

文章信息/Info

Title:
Research Progress on Medical Entity Recognition
文章编号:
1673-629X(2020)04-0057-06
作者:
张明淘1 ; 韩 普1; 2
1. 南京邮电大学 管理学院,江苏 南京 210003; 2. 江苏省数据工程与知识服务重点实验室,江苏 南京 210023
Author(s):
ZHANG Ming-tao1 ; HAN Pu1; 2
1. School of Management,Nanjing University of Posts & Telecommunications,Nanjing 210003,China; 2. Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service,Nanjing 210023,China
关键词:
医疗实体识别; 深度学习; 神经网络; 医疗大数据; 人工智能
Keywords:
medical entity recognition; deep learning; neural network; medical big data; artificial intelligence
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 04. 011
摘要:
深入了解医疗实体识别的现状和进展,有助于进一步提升医疗实体识别的效果。 通过梳理国内外医疗实体识别的相关研究进展和研究成果,并分别从医疗实体概念和分类、国内外重要医疗实体识别评测会议,以及传统的和当代的医疗实体识别方法三方面进行归纳和总结,系统全面地阐述了医疗实体识别的研究现状,指出了当前研究存在的问题,并对医疗实体识别的未来发展趋势进行了展望。 从深度学习的视角,仅仅对循环神经网络、长短时记忆神经网络等主流的神经网络模型在医疗实体识别领域的应用进行了分析和总结。 在医疗大数据和人工智能背景下,医疗实体识别是医疗领域信息处理和人工智能的基础,并且该研究已经发展成为自然语言处理中新的研究方向,对医疗大数据分析和医疗人工智能具有重要意义。
Abstract:
Further understanding of the status and progress of medical entity recognition is helpful to further improve the effectiveness of medical entity recognition. By analyzing medical entity recognition at home and abroad related research progress and research achievements,and respectively concluding and summarizing from three aspects, namely, medical entity concept and classification, the important medical entity recognition at home and abroad review meeting, and traditional and contemporary medical entity recognition methods,we comprehensively expound the research status of medical entity recognition and point out the problems existing in the current study. Finally,the future development trend of medical entity recognition is discussed. From the perspective of deep learning, theapplication of mainstream neural network models such as recurrent neural network and long short-time memory in the field of medical entity recognition is analyzed and summarized. In the context of medical big data and artificial intelligence, medical entity recognition is the basis of medical information processing and artificial intelligence,and this research has developed into a new research direction in natural language processing,which is of great significance for medical big data analysis and medical artificial intelligence.

相似文献/References:

[1]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[2]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
[3]黄法秀,张世杰,吴志红,等.数据增广下的人脸识别研究[J].计算机技术与发展,2020,30(03):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
　HUANG Fa-xiu,ZHANG Shi-jie,WU Zhi-hong,et al.Research on Face Recognition Based on Data Augmentation[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(04):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
[4]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2016,26(04):19.
[5]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
[6]贺飞翔,赵启军. 基于深度学习的头部姿态估计[J].计算机技术与发展,2016,26(11):1.
　HE Fei-xiang,ZHAO Qi-jun. Head Pose Estimation Based on Deep Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2016,26(04):1.
[7]徐 融,邱晓晖.一种改进的 YOLO V3 目标检测方法[J].计算机技术与发展,2020,30(07):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
　XU Rong,QIU Xiao-hui.An Improved YOLO V3 Object Detection[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(04):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
[8]曾志平[] [],萧海东[],张新鹏[]. 基于DBN的金融时序数据建模与决策[J].计算机技术与发展,2017,27(04):1.
　ZENG Zhi-ping[] [],XIAO Hai-dong[],ZHANG Xin-peng[]. Modeling and Decision-making of Financial Time Series Data with DBN[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2017,27(04):1.
[9]李全兵,文 钊*,田艳梅*,等.基于 WGAN 的音频关键词识别研究[J].计算机技术与发展,2021,31(08):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
　LI Quan-bing,WEN Zhao *,TIAN Yan-mei *,et al.Research on Audio Keywords Recognition Based on WassersteinGenerative Adversarial Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2021,31(04):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
[10]李宏林. 分析式纹理合成技术及其在深度学习的应用[J].计算机技术与发展,2017,27(11):7.
　LI Hong-lin. Analyzed Texture-synthesis Techniques and Their Applications in Deep Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2017,27(04):7.

更新日期/Last Update: 2020-04-10