Abstract
Previous studies on clinical sequence labeling require large amounts of task specific knowledge in the form of handcrafted features. Using latest development in representation learning, this paper introduces BERT embedding as character based pretrained model and incorporates it with three competing deep learning models (CNN-LSTM, Bi-LSTM and Bi-LSTM-CRF) to extract clinical entities from electronic health records. A comparative evaluation based on CCKS-2017 task 2 benchmark dataset reveals that: (1) BERT embedding not only facilitates improving performance of clinical NER tasks but also acts as good candidate for building end-to-end NER model requiring no feature engineering from Chinese EHR. (2) Bi-LSTM-CRF has the highest performance, i.e., 93% F1 scores when it uses BERT embedding. This paper may enhance our understanding of how to use BERT embedding in clinical NER researches.
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Notes
- 1.
Dataset available at http://www.ccks2017.com/en/index.php/sharedtask/.
- 2.
Accessed at https://openbayes.com/.
- 3.
BERT-Base, Chinese is available at https://github.com/google-research/bert.
References
Stubbs, A., Kotfila, C., Xu, H., Uzuner, Ö.: Identifying risk factors for heart disease over time: overview of 2014 i2b2/UTHealth shared task Track 2. J. Biomed. Inf. 58, S67–S77 (2015)
Jung, K., LePendu, P., Iyer, S., Bauer-Mehren, A., Percha, B., Shah, N.H.: Functional evaluation of out-of-the-box text-mining tools for data-mining tasks. J. Am. Med. Inf. Assoc. 22, 121–131 (2015)
Ben Abacha, A., Zweigenbaum, P.: Medical entity recognition: a comparison of semantic and statistical methods. In: Proceedings BioNLP 2011 Work, pp. 56–64(2011)
Skeppstedt, M., Kvist, M., Nilsson, G.H., Dalianis, H.: Automatic recognition of disorders, an annotation and machine learning study findings, pharmaceuticals and body structures from clinical text. J. Biomed. Inf. 49, 148–158 (2014)
Chen, Y.K., Lasko, T.A., Mei, Q.Z., Denny, J.C., Xu, H.: A study of active learning methods for named entity recognition. J. Biomed. Inform. 58, 11–18 (2016)
Erik, F., Sang, T.K., Meulder, F.D.: Introduction to the conll-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4, pp. 142–147. Association for Computational Linguistics (2003)
Ma, X., Xia, F.: Unsupervised dependency parsing with transferring distribution via parallel guidance and entropy regularization. In: Proceedings of ACL-2014, pp. 1337–1348, Baltimore, June 2014
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2), 157–166 (1994)
Passos, A., Kumar, V., McCallum, A.: Lexicon infused phrase embeddings for named entity resolution. In: Proceedings of CoNLL-2014, pp. 78–86, Ann Arbor, June 2014
Xu, K., Yang, Z., Kang, P., Wang, Q., et al.: Document-level attention-based Bi LSTM-CRF incorporating disease dictionary for disease named entity recognition. Comput. Biol. Med. 108, 122–132 (2019)
Na, S.H., Kim, H., Min, J., et al.: Improving LSTM CRFs using character-based compositions for Korean named entity recognition. Comput. Speech Lang. 54, 106–121 (2019)
Shi, X., Chen, Z., Wang, H., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting (2015)
Sainath, T.N., Vinyals, O., Senior, A., et al.: Convolutional, long short-term memory, fully connected deep neural networks. In: International Conference on Acoustics (2015)
Unanue, I.J., Borzeshi, E.Z., Piccardi, M.: Recurrent neural networks with specialized word embeddings for health domain named-entity recognition. J. Biomed. Inform. 76, 102–109 (2017)
Donahue, J., Hendricks, L.A., Guadarrama, S.: Long-term recurrent convolutional networks for visual recognition and description. In: AB Into Calculation of the Structures and Properties of Molecules (2015)
Vinyals, O., Toshev, A., Bengio, S., et al.: Show and tell: a neural image caption generator (2014)
Ruch, P., Baud, R., Geissbuhler, A.: Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record. Artif. Intell. Med. 29, 169–184 (2003)
Liu, H., Mi, X., Li, Y.: Smart deep learning-based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short-term memory network. Energy Convers. Manage. 166, 120–131 (2018)
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Wu, J., Shao, Dr., Guo, Jh., Cheng, Y., Huang, G. (2019). Character-Based Deep Learning Approaches for Clinical Named Entity Recognition: A Comparative Study Using Chinese EHR Texts. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds) Smart Health. ICSH 2019. Lecture Notes in Computer Science(), vol 11924. Springer, Cham. https://doi.org/10.1007/978-3-030-34482-5_28
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