Abstract
Electronic medical record named entity recognition can extract important clinical information from unstructured text, which is helpful for clinical diagnosis and medical decision-making. However, due to the particularity of the medical field, it is difficult for researchers to obtain sufficient labeled electronic medical records. Models trained using traditional supervised learning methods with insufficient data are not promising. To solve this problem, this paper proposes two weakly supervised learning methods, sampling-based active learning and parameter-based transfer learning, to achieve better performance. In sampling-based active learning, two uncertainty sampling strategies, least confidence sampling and entropy sampling, are used to select data from unlabeled dataset for retraining. In parameter-based transfer learning, the parameters of word representation layer and encoding layer in the source domain are initialized to the corresponding layer of the target domain, and the objective is to learn generalized linguistic knowledge from the source domain. Finally, we use a voting mechanism to ensemble these individual models to get better prediction results. Experiment on the CCKS2017 official test set shows that our system for MER achieves 0.8972 F1 score and gets better performance than the supervised methods, which obtains 0.8921 F1 score and proves the effectiveness of our approaches. The experimental results show that the weakly supervised learning methods proposed in this paper achieve the satisfactory performance as the supervised methods under comparable conditions.
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References
Yang J, Yu Q, Guan Y, Jiang Z. An overview of research on electronic medical record oriented named entity recognition and entity relation extraction. Acta Automat Sin. 2014;40(8):1537–62.
Yang JF, Guan Y, He B, Qu C, Yu Q, Liu Y, Zhao Y. Corpus construction for named entities and entity relations on Chinese electronic medical records. J Softw. 2016;27(11):2725–46.
Chowdhury Shanta, Dong Xishuang, Qian Lijun, Li Xiangfang, Guan Yi, Yang Jinfeng, Qiubin Yu. A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records. BMC Bioinf. 2018;19(17):75–84.
Wan L, Luo Y, Zhi L. The recognition of naming entity of BI-LSTM Chinese electronic medical records based on the joint training of Chinese characters and words. China Digital Medicine. 2019;14(2):54–6.
Li Y, Bontcheva K, Cunningham H. SVM based learning system for information extraction. In: International Workshop on Deterministic and Statistical Methods in Machine Learning. Springer; 2004. p. 319–339.
McCallum A, Li W. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003. 2003. p. 188–91.
Bikel DM, Miller S, Schwartz R, Weischedel R. Nymble: a high-performance learning name-finder. In: Fifth Conference on Applied Natural Language Processing. 1997. p. 194–201.
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. J Mach Learn Res 2011;12(ARTICLE):2493–2537.
Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991. 2015.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.
Chiu JPC, Nichols E. Named entity recognition with bidirectional LSTM-CNNS. Transactions of the Association for Computational Linguistics. 2016;4:357–70.
Dong C, Zhang J, Zong C, Hattori M, Di H. Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Natural Language Understanding and Intelligent Applications. Springer; 2016. p. 239–250.
Shen D, Zhang J, Su J, Zhou G, Tan CL. Multi-criteria-based active learning for named entity recognition. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04). 2004. p. 589–596.
Tomanek K, Hahn U. Semi-supervised active learning for sequence labeling. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009. p. 1039–1047.
Shen Y, Yun H, Lipton ZC, Kronrod Y, Anandkumar A. Deep active learning for named entity recognition. In: Proceedings of the 2nd Workshop on Representation Learning for NLP. 2017. p. 252–256.
Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2009;22(10):1345–59.
Dai W, Yang Q, Xue G, Yu Y. Boosting for transfer learning. In: Ghahramani Z, editor. Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007, volume 227 of ACM International Conference Proceeding Series. ACM; 2007. p. 193–200.
Yang Z, Salakhutdinov R, Cohen WW. Transfer learning for sequence tagging with hierarchical recurrent networks. arXiv preprint arXiv:1703.06345 . 2017.
Coden A, Savova G, Sominsky I, Tanenblatt M, Masanz J, Schuler K, Cooper J, Guan W, De Groen PC. Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. J Biomed Inform. 42(5):937–949, 2009.
Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG. Mayo clinical text analysis and knowledge extraction system (CTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17(5):507–513.
De Bruijn Berry, Cherry Colin, Kiritchenko Svetlana, Martin Joel, Zhu Xiaodan. Machine-learned solutions for three stages of clinical information extraction: the state of the art at I2B2 2010. J Am Med Inform Assoc. 2011;18(5):557–62.
Jonnalagadda S, Cohen T, Stephen W, Gonzalez G. Enhancing clinical concept extraction with distributional semantics. J Biomed Inform. 2012;45(1):129–40.
Chalapathy R, Borzeshi EZ, Piccardi M. Bidirectional LSTM-CRF for clinical concept extraction. In: Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP). 2016. p. 7–12.
Lei J, Tang B, Xueqin L, Gao K, Jiang M, Hua X. A comprehensive study of named entity recognition in Chinese clinical text. J Am Med Inform Assoc. 2014;21(5):808–14.
Yonghui W, Jiang M, Lei J, Hua X. Named entity recognition in Chinese clinical text using deep neural network. Stud Health Technol Inform. 2015;216:624.
Liu K, Hu Q, Liu J, Xing C. Named entity recognition in Chinese electronic medical records based on CRF. In: 2017 14th Web Information Systems and Applications Conference (WISA). IEEE; 2017. p. 105–110.
Jianglu H, Shi X, Liu Z, Wang X, Chen Q, Tang B. HITSZ CNER: a hybrid system for entity recognition from Chinese clinical text. In: CEUR Workshop Proceedings, vol. 1976. 2017. p. 25–30.
Jinhang W, Xiao H, Zhao R, Ren F, Minghan H. Clinical named entity recognition via bi-directional LSTM-CRF model. In: CEUR Workshop Proceedings, vol. 1976. 2017. p. 31–6.
Devlin J, Chang M-W, Kenton L, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT. 2019. p. 4171–4186.
Settles B, Craven M. An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. 2008. p. 1070–1079.
Dor LE, Halfon A, Gera A, Shnarch E, Dankin L, Choshen L, Danilevsky M, Aharonov R, Katz Y, Slonim N. Active learning for BERT: an empirical study. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. pp 7949–7962.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 61772505 and Grant 62076233 and Beijing Information Science and Technology University Practical Training Project. Moreover, we thank all reviewers for their valuable comments and suggestions.
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Ma, LL., Yang, J., An, B. et al. Medical Named Entity Recognition Using Weakly Supervised Learning. Cogn Comput 14, 1068–1079 (2022). https://doi.org/10.1007/s12559-022-10003-9
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DOI: https://doi.org/10.1007/s12559-022-10003-9