[1] TANG B, WANG X, YAN J, et al. Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF[J]. BMC Medical Informatics and Decision Making, 2019, 19(S3): 74.
[2] XUE M, CAI W, SU J, et al. Neural collective entity linking based on recurrent random walk network learning[J]. arXiv:1906.09320, 2019.
[3] ZHU T, QIN Y, XIANG Y, et al. Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning[J]. Journal of the American Medical Informatics Association, 2021, 28(12): 2571-2581.
[4] BARTOLO M, THRUSH T, JIA R, et al. Improving question answering model robustness with synthetic adversarial data generation[J]. arXiv:2104.08678, 2021.
[5] LIN Z H, YANG D, YIN X C. Patient similarity via joint embeddings of medical knowledge graph and medical entity descriptions[J]. IEEE Access, 2020, 8: 156663-156676.
[6] NADEAU D, SEKINE S. A survey of named entity recognition and classification[J]. Lingvisticae Investigationes, 2007, 30(1): 3-26.
[7] NADEAU D, TURNEY P D, MATWIN S. Unsupervised named-entity recognition: generating gazetteers and resolving ambiguity[C]//Proceedings of the 19th International Conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence, 2006: 266-277.
[8] ZHENG X, DU H, LUO X, et al. BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework[J]. BMC Bioinformatics, 2022, 23(1): 1-19.
[9] ZHANG Z Y, CHEN A L P. Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning[J]. BMC Bioinformatics, 2022, 23(1): 458.
[10] 崔少国, 陈俊桦, 李晓虹. 融合语义及边界信息的中文电子病历命名实体识别[J]. 电子科技大学学报, 2022, 51(4): 565-571.
CUI S G, CHEN J H, LI X H. Named entity recognition for Chinese electronic medical record by fusing semantic and boundary information[J]. Journal of University of Electronic Science and Technology of China, 2022, 51(4): 565-571.
[11] 孙振, 李新福. 多特征融合的中文电子病历命名实体识别[J]. 计算机工程与应用, 2023, 59(23): 136-144.
SUN Z, LI X F. Named entity recognition of Chinese electronic medical records based on multi-feature fusion[J]. Computer Engineering and Applications, 2023, 59(23): 136-144.
[12] 雷松泽, 刘博, 王瑜菲, 等. 结合多特征嵌入和多网络融合的中文医疗命名实体识别[J]. 电子与信息学报, 2023(8): 3032-3039.
LEI S Z, LIU B, WANG Y F, et al. Chinese medical named entity recognition combined with multi-feature embedding and multi-network fusion[J]. Journal of Electronics & Information Technology, 2023(8): 3032-3039.
[13] 封红旗, 孙杨, 杨森, 等. 基于BERT的中文电子病历命名实体识别[J]. 计算机工程与设计, 2023, 44(4): 1220-1227.
FENG H Q, SUN Y, YANG S, et al. Chinese electronic medical record named entity recognition based on BERT methods[J]. Computer Engineering and Design, 2023, 44(4): 1220-1227.
[14] ZHANG Y, YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 1118-1144.
[15] MA R T, PENG M L, ZHANG Q, et al. Simplify the usage of lexicon in Chinese NER[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 5951-5960.
[16] GUI T, ZOU Y, ZHANG Q, et al. A lexicon-based graph neural network for Chinese NER[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 1040-1050.
[17] 陈雪松, 朱鑫海, 王浩畅. 基于PMV-LSTM的中文医学命名实体识别[J]. 计算机工程与设计, 2022, 43(11): 3257-3263.
CHEN X S, ZHU X H, WANG H C. Chinese medical named entity recognition based on PMV-LSTM[J]. Computer Engineering and Design, 2022, 43(11): 3257-3263.
[18] SI Y, WANG J, XU H, et al. Enhancing clinical concept extraction with contextual embeddings[J]. Journal of the American Medical Informatics Association, 2019, 26(11): 1297-1304.
[19] PENNINGTON J, SOCHER R, MANNING C. Glove: global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014: 1532-1543.
[20] 罗凌, 杨志豪, 宋雅文, 等. 基于笔画ELMo和多任务学习的中文电子病历命名实体识别研究[J]. 计算机学报, 2020, 43(10): 1943-1957.
LUO L, YANG Z H, SONG Y W, et al. Chinese clinical named entity recognition based on stroke ELMo and Multi-Task learning[J]. Chinese Journal of Computers, 2020, 43(10): 1943-1957.
[21] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv:1810.04805, 2018.
[22] ZHANG X, ZHANG Y, ZHANG Q, et al. Extracting comprehensive clinical information for breast cancer using deep learning methods[J]. International Journal of Medical Informatics, 2019, 132: 1-7.
[23] LI D, LONG J, QU J, et al. Chinese clinical named entity recognition with ALBERT and MHA mechanism[J]. Evidence Based Complementary and Alternative Medicine, 2022, 2022: 2056039.
[24] 张付领. 结合ERNIE2.0的医疗中文命名实体识别模型[J]. 电子设计工程, 2023, 31(4): 38-42.
ZHANG F L. Medical Chinese named entity recognition model combined with ERNIE2.0[J]. Electronic Design Engineering, 2023, 31(4): 38-42.
[25] 杨飞洪. 面向中文临床自然语言处理的BERT模型研究[D]. 北京: 北京协和医学院, 2021.
YANG F H. A research on BERT model for Chinese clinical language processing[D]. Beijing: Peking Union Medical College, 2021.
[26] DEMIR A, KOIKE-AKINO T, WANG Y, et al. EEG-GAT: graph attention networks for classification of electroencephalogram (EEG) signals[C]//Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022: 30-35.
[27] KIM J, SEO H, NASEEM M T, et al. Pathological-gait recognition using spatiotemporal graph convolutional networks and attention model[J]. Sensors, 2022, 22(13): 4863.
[28] LI Z, LIU H, ZHANG Z, et al. Learning knowledge graph embedding with heterogeneous relation attention networks[C]//Proceedings of the IEEE Transactions on Neural Networks and Learning Systems, 2022: 3961-3973.
[29] XIONG Y, PENG H, XIANG Y, et al. Leveraging multi-source knowledge for Chinese clinical named entity recognition via relational graph convolutional network[J]. Journal of Biomedical Informatics, 2022, 128: 104035.
[30] LI Y, HUI L, ZOU L, et al. Relation extraction in biomedical texts based on multi-head attention model with syntactic dependency feature: modeling study[J]. JMIR Medical Informatics, 2022, 10(10): 41136.
[31] VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph attention networks[J]. arXiv:1710.10903, 2017.
[32] ZHANG N, JIA Q, YIN K, et al. Conceptualized representation learning for Chinese biomedical text mining[J]. arXiv:2008.10813, 2020.
[33] LI X, YAN H, QIU X, et al. FLAT: Chinese NER using flat-lattice transformer[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 6836-6842.
[34] ZHANG T, CAI Z, WANG C, et al. SMedBERT: a knowledge-enhanced pre-trained language model with structured semantics for medical text mining[J]. arXiv:2108.08983, 2021.
[35] ZHANG Z, HAN X, LIU Z, et al. ERNIE: enhanced language representation with informative entities[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 1441-1451. |