Abstract
Named Entity Recognition(NER) for Chinese Electronic Medical Record (EMR) is to identify medical entities and entity boundaries. Nested entities exist in Chinese electronic medical records, which makes NER low accuracy. To improve the recognition accuracy, this paper proposes a NER method, named CCRFs-NER. Firstly, the feature set is constructed by word features, part-of-speech features, and entity-identifier features. Then, Bi-LSTM and attention mechanism are used to process the feature set to extract the global and local features. Finally, the features are input into the CCRFs for probability prediction, and the recognition results are obtained. The experimental results show that the CCRFs-NER can improve the accuracy of named entity recognition for Chinese electronic medical records.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, R., Zhao, P., Guo, W., Wang, R., Lu, W.: Medical Named Entity Recognition based on Dilated Convolutional Neural Network. Cognitive Robotics 2, 13–20 (2022)
Li, F., Wang, Z., Hui, S., Liao, L., Zhu, X., Huang, H.: A segment enhanced span-based model for nested named entity recognition. Neurocomputing 465, 26–37 (2021)
Li, D., Yan, L., Yang, J., Ma, Z.: Dependency Syntax Guided BERT-BILSTM-GAM-CRF for Chinese NER. Expert Syst. Appl. 196(15), 116682 (2022)
Yu, G., et al.: Adversarial Active Learning for the Identification of Medical Concepts and Annotation Inconsistency. J. Biomed. Inform. 108, 103481 (2021)
Jiang, D., Ren, H., Cai, Y., Xu, J., Liu, Y., Leung, H.: Candidate region aware nested named entity recognition. Neural Netw. 142, 340–350 (2021)
Zhang, S., Zhu, H., Xu, H., Zhu, G.: A named entity recognition method towards product reviews based on BiLSTM-Attention-CRF 2021, Int. J. Comput. Sci. Eng. https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcse (Accessed 4 March 2022)
Zhu, G., Liu, W., Zhang, S., Chen, X., Yi, C.: The method for extracting new login sentiment words from Chinese micro-blog based on improved mutual information. Int. J. Comput Syst Sci Eng 35(3), 223–232 (2020)
Carol, F., Alderson, P., Austin, J., Cimino, J., Johnson, S.: A General Natural-language Text Processor for Clinical Radiology. J Am Med Inf. Assoc. 1(2), 161–174 (1994)
Hua, X., Stenner, S., Doan, S., Johnson, K., Waitman, L., Denny, J.: MedEx: a Medication Information Extraction System for Clinical Narratives. J Am Med Inf Assoc 17(1), 19–24 (2010)
Yan, X., Xiong, X., Cheng, X., Huang, Y., Zhu, H., Hu, F.: HMM-BiMM: Hidden Markov Model-based Word Segmentation Via Improved Bi-directional Maximal Matching Algorithm. Comput. Electr. Eng. 94, 107354 (2021)
Sharma, R., Morwal, S., Agarwal, B.: Named Entity Recognition using Neural Language Model and CRF for Hindi Language. Comput. Speech Lang. 74, 101356 (2021)
Gandhi, H., Attar, H.: Extracting Aspect Terms using CRF and Bi-LSTM Models. Proc Comput Sci 167, 2486–2495 (2021)
Xu, K., Yang, Z., Kang, P., Wang, Q., Liu, W.: Document-level Attention-based BiLSTM-CRF Incorporating Disease Dictionary for Disease Named Entity Recognition. Comput. Biol. Med. 108, 122–132 (2019)
Catelli, R., Casola, V., Pietro, G., Fujita, H., Esposito, M.: Combining Contextualized Word Representation and Sub-document Level Analysis Through Bi-LSTM+CRF Architecture for Clinical De-identification. Knowl.-Based Syst. 213, 106649 (2021)
Tao, C., Filannino, M., Uzun, N.: Prescription Extraction using CRFs and Word Embeddings. J. Biomed. Inform. 72, 60–65 (2017)
Aras, G., Makaroglu, D., Demir, S., Cakir, A.: An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition. Expert Syst. Appl. 182, 115049 (2021)
Acknowledgements
This work was supported by the Graduate Students Scientific Research Program of Anhui Province(YJS20210402), the National Natural Science Foundation of China (Grant NO.62076006), the University Synergy Innovation Program of Anhui Province (GXXT-2021–008), and the Anhui Provincial Key R&D Program(202004b11020029).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Sun, Z., Zhu, G. (2023). CCRFs-NER: Named Entity Recognition Method Based on Cascaded Conditional Random Fields Oriented Chinese EMR. In: Abawajy, J.H., Xu, Z., Atiquzzaman, M., Zhang, X. (eds) Tenth International Conference on Applications and Techniques in Cyber Intelligence (ICATCI 2022). ICATCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 169. Springer, Cham. https://doi.org/10.1007/978-3-031-28893-7_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-28893-7_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28892-0
Online ISBN: 978-3-031-28893-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)