Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR

  • Zhichang ZhangEmail author
  • Yu Zhang
  • Tong Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)


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.


Chinese electronic medical record Named entity recognition Deep learning Knowledge attention 



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).


  1. 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)CrossRefGoogle Scholar
  2. Carlson, A., Betteridge, J., Wang, R.C., et al.: Coupled semi-supervised learning for information extraction. DBLP, pp. 101–110 (2010)Google Scholar
  3. 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). Scholar
  4. 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)Google Scholar
  5. 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)Google Scholar
  6. 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).
  7. 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)CrossRefGoogle Scholar
  8. 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). Scholar
  9. 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)Google Scholar
  10. 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)CrossRefGoogle Scholar
  11. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF (2016).
  12. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investig. 30(1), 3–26 (2007)CrossRefGoogle Scholar
  13. 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). Article ID 240403Google Scholar
  14. 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)Google Scholar
  15. 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)Google Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)Google Scholar

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Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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