Abstract
Entity recognition of clinical documents is a primary task to extract information from unstructured clinical documents. Traditional entity recognition methods extract entities in a supervised learning framework which needs a large scale of labeled corpus as the training samples. However, clinical documents in real world are unlabeled. To construct a large scale of labeled corpus by manual is time-consuming. Semi-supervised learning that relies on small-scale corpus can solve such problem. Thus, this paper proposes an entity recognition model of clinical documents based on self-training framework. In such framework, we first establish partial annotation corpus through the way of dependency syntax analysis and the medical statement rule unifies. Then, a hybrid model of CNN-LSTM-CRF is proposed to label the unlabeled data in an end-to-end way. Specially, we will use CNN to embed characters in clinical document and use Bi-LSTM to extract the sentence-level feature. At the moment, we use CRF remedies the shortage of LSTM which further combined with the combination probability of CRF and the advantages of optimizing the whole sequence. Finally, the results of entity recognition with higher confidence level are fed back by self-training to expend size of corpus which improves the accuracy of the document entity recognition. The experiment result proves the availability and high efficiency of this model.
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References
Yang Jinfeng, Yu Qiubin, Guan Yi, He Bin, et al. An Overview of Research on Electronic Medical Record Oriented Named Entity Recognition and Entity Relation Extraction [J]. Acta Automatica Sinica, 2014, 40(8):1537–1562
Sebastian Polsterl, Sailesh Conjeti, Nassir Navab. Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection. Artificial Intelligence in Medicine, 2016, 72:1–11
Travis Goodwin, Sanda Harabagiu. KnowledgeGraph and its usage for Retrieving patient Cohorts from Electronic medical Records. International Conference on Semantic computing, 2013:363–370
Cheng Ziguang, Zheng Dequan, Li Sheng. Multi-pattern Fusion Based Semi-Supervised Name Entity Recognition [C]. International Conference on Machine Learning and Cybernetics, NJ:IEEE, 2013:45–50
Zeng Donghuo, Sun Chengjie, Li Lei, et al. Enlarging Drug Dictionary with Semi- Super vised Learning for Drug Entity Recognition [C]. International Conference on Bioinformatics and Biomedicine, 2016:1929–1931
Yao Lin, Liu Hong, Liu Yi, et al. Biomedical Named Entity Recognition based on Deep Neutral Network [J]. International Journal of Hybrid Information Technology, 2015, 8(8):279–288
Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi. Bidirectional LSTM-CRF for Clinical Concept Extraction [J]. Proceedings of the Clinical Natural Language Processing Workshop, 2016, 7–12
Jason P.C. Chiu, Eric Nichols. Named Entity Recognition with Bidirectional LSTM-CNNs [J]. Transactions of the Association for Computational Linguistics, 2016, 4:357–370
Tang Zhuo, Jiang Lingang, Yang Li, et al. CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework [J]. Cluster Computing, 2015, 18(2):493–505
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Che, N., Chen, D., Le, J. (2019). Entity Recognition Approach of Clinical Documents Based on Self-training Framework. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_31
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DOI: https://doi.org/10.1007/978-981-10-8944-2_31
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Online ISBN: 978-981-10-8944-2
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