Abstract
Named entity recognition (NER) in Chinese electronic medical records (EMRs) has become an important task of clinical natural language processing (NLP). However, limited studies have been performed on the clinical NER study in Chinese EMRs. Furthermore, when end-to-end neural network models have improved clinical NER performance, medical knowledge dictionaries such as various disease association dictionaries, which provide rich information of medical entities and relations among them, are rarely utilized in NER model. In this study, we investigate the problem of NER in Chinese EMRs and propose a clinical neural network NER model enhanced with medical knowledge attention by combining the entity mention information contained in external medical knowledge bases with EMR context together. Experimental results on the manually labeled dataset demonstrated that the proposed method can achieve better performance than the previous methods in most cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, Y.-G., Liu, F., Simpson, P., Antieau, L., Bennett, A.: AskHERMES: an online question answering system for complex clinical questions. J. Biomed. Inform. 44(2), 277–288 (2011)
Carlson, A., Betteridge, J., Wang, R.C., et al.: Coupled semi-supervised learning for information extraction. DBLP, pp. 101–110 (2010)
Chabchoub, M., Gagnon, M., Zouaq, A.: Collective disambiguation and semantic annotation for entity linking and typing. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 33–47. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46565-4_3
Chang, F.-X., Guo, J., Xu, W.-R., Chung, S.-R.: Application of word embeddings in biomedical named entity recognition tasks. J. Digit. Inf. Manag. 13(5), 321–327 (2015)
Dong, X., Chowdhury, S., Qian, L., et al.: Transfer bi-directional LSTM RNN for named entity recognition in Chinese electronic medical records. In: The Proceedings of International Conference on E-Health Networking, Applications and Services, pp. 1–4. IEEE (2017)
Le, H.-Q., Nguyen, T., Vu, S., Dang, T.-H.: D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics (2018). https://doi.org/10.1093/bioinformatics/bty356
Lei, J., Tang, B., Lu, X., Gao, K., Jiang, M., Xu, H.: A comprehensive study of named entity recognition in Chinese clinical text. J. Am. Med. Inform. Assoc. 21(5), 808–814 (2014)
Li, L., Jin, L., Jiang, Y., Huang, D.: Recognizing biomedical named entities based on the sentence vector/twin word embeddings conditioned bidirectional LSTM. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds.) CCL/NLP-NABD-2016. LNCS (LNAI), vol. 10035, pp. 165–176. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47674-2_15
Liu, Y., Liu, K., Xu, L.-H. Zhao, J.: Exploring fine-grained entity type constraints for distantly supervised relation extraction. In: Proceedings of COLING 2014, Dublin, Ireland, 23–29 August (2014)
Liu, Z., Tang, B., Wang, X., et al.: De-identification of clinical notes via recurrent neural network and conditional random field. J. Biomed. Inform. 75S, S34 (2017)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF (2016). https://arxiv.org/pdf/1603.01354
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investig. 30(1), 3–26 (2007)
Tang, B.-Z., Cao, H., Wang, X.-L., Chen, Q.-C., Xu, H.: Evaluating word representation features in biomedical named entity recognition tasks. Biomed Res. Int. 2014, 6 (2014). https://doi.org/10.1155/2014/240403. Article ID 240403
Wang, S., Li, S., Chen, T.: Recognition of Chinese medicine named entity based on condition random field. J Xiamen Univ. (Nat. Sci.) 48, 349–364 (2009)
Wang, Y., Liu, Y., Yu, Z., et al.: A preliminary work on symptom name recognition from free-text clinical records of traditional Chinese medicine using conditional random fields and reasonable features. In: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, Stroudsburg, PA, USA, pp. 223–30 (2012)
Xu, Y., Wang, Y., Liu, T., et al.: Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries. J. Am. Med. Inform. Assoc. 21, e84–e92 (2014)
Yao, L., Liu, H., Liu, Y., et al.: Biomedical named entity recognition based on deep neutral network. Int. J. Hybrid Inf. Technol. 8, 279–288 (2015)
Ye, F., Chen, Y.Y., Zhou, G.G., et al.: Intelligent recognition of named entity in electronic medical records. Chin. J. Biomed. Eng. 30(2), 256–262 (2011)
Acknowledgements
We would like to thank the anonymous reviewers for their valuable comments. The research work is supported by the National Natural Science Foundation of China (No. 61762081, No. 61662067) and the Key Research and Development Project of Gansu Province (No. 17YF1GA016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Z., Zhang, Y., Zhou, T. (2018). Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-01716-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01715-6
Online ISBN: 978-3-030-01716-3
eBook Packages: Computer ScienceComputer Science (R0)