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Healthcare entity recognition based on deep learning

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Abstract

China has entered a stage of high-quality development, and people have higher demand and expectations for the existing medical field. MNER(Medical Named Entity Recognition) is the task of identifying the correct entity boundary and classifying medical entities from a piece of medical text information. The effect of MNER would directly affect the performance of downstream relationship extraction and intelligent question answering, which has important research significance and value. In this paper, aiming at the named entity recognition of discontinuous medical entities in Chinese medical text information, this paper expands the research based on BiLSTM-CRF, introduces the IDCNN layer after the input layer of the model to capture the local context information in the medical text, and then uses the output of IDCNN as the input of BiLSTM-CRF for subsequent training, to construct an IDCNN-BiLSTM-CRF network model for discontinuous medical text. Based on BERT, the weight is assigned to the 12-layer transformer in BERT, and the results are weighted and averaged, and a WfBERT-Att-D-BiLSTM-CRF model based on the weight output of different layers of BERT is proposed. During the experiment, the hidden layer, the number of iterations, and the batch data size are continuously experimented with, and finally, the optimal parameter settings are obtained. In this paper, repeated experiments are carried out on different datasets, the final actual recognition effect is also tested, and the correctness of the proposed model is verified from different perspectives.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Zhang X, Liu S, Wang H (2023) Personalized learning path recommendation for e-learning based on knowledge graph and graph convolutional network[J]. Int J Software Eng Knowl Eng 33(01):109–131

    Article  Google Scholar 

  2. Lee LH, Lu Y (2021) Multiple embeddings enhanced multi-graph neural networks for Chinese Healthcare named entity recognition[J]. IEEE J Biomed Health Inform 25(7):2801–2810

    Article  PubMed  Google Scholar 

  3. Ramachandran R, Arutchelvan K (2021) Grey Wolf optimizer with conditional random fields for healthcare related medical named entity recognition model[J]. SSRN Electron J. https://doi.org/10.2139/ssrn.3769845

  4. Quimbaya AP, Múnera AS, Rivera RA, Rodríguez JC, Velandia OM, Peña AA, Labbé C (2016) Named entity recognition over electronic health records through a combined dictionary-based approach[J]. Procedia Comput Sci 100:55–61. https://doi.org/10.1016/j.procs.2016.09.123

    Article  Google Scholar 

  5. Wang X, Chused A, Elhadad N, Friedman C, Markatou M (2008) Automated knowledge acquisition from clinical narrative reports. In: Proceedings of the 2008 AMIA Annual Symposium, p 783–787

  6. Bhatia S, Sharma M, Bhatia KK (2015) Strategies for mining opinions: A survey. In: Proc. of the 2nd Int'l Conf. on Computing for Sustainable Global Development (INDIACom 2015). Washington: IEEE Computer Society pp 262–266

  7. Rebecka W, Linus H, Agnes T et al (2014) Visual entity linking: A preliminary study[C]. Proceedings of Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence(AAAI-14): 46–49

  8. Li L, Zhao J, Hou L et al (2019) An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records[J]. BMC Med Inform Decis Mak 19(Suppl 5):235. https://doi.org/10.1186/s12911-019-0933-6

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pomares-Quimbaya A, Gonzalez RA, Velandia OMM et al (2018) Concept attribute labeling and context-aware named entity recognition in electronic health records[J].  Int J Reliab Qual E-Healthc (IJRQEH) 7

  10. Wang Q, Zhou Y, Ruan T, Gao D, Xia Y, He P (2019) Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition[J]. J Biomed Inform 92

  11. Tay Y, Tuan LA, Hui SC (2018) Multi-cast attention networks for retrieval-based question answering and response prediction[J]. https://doi.org/10.48550/arXiv.1806.00778

  12. Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions[C]//ICLR. https://doi.org/10.48550/arXiv.1511.07122

  13. Yang B, Mitchell T (2019) Leveraging knowledge bases in LSTMs for improving machine reading[J]. https://doi.org/10.48550/arXiv.1902.09091

  14. Yang YS, Zhang M, Chen W et al (2018) Adversarial learning for Chinese NER from crowd annotations. https://doi.org/10.48550/arXiv.1801.05147

  15. Zhang Y, Yang J (2018) Chinese ner using lattice lstm[J]. arXiv preprint arXiv:1805.02023

  16. Xia Y, Wang Q (2017) Clinical named entity recognition: ECUST in the CCKS-2017 shared task 2[C]. CEUR Workshop Proceedings. Chengdu, China: the Technical Committee on Language and Knowledge Computing of The Chinese Information Processing Society of China 1976:43–48

  17. Huang L, Yu C, Chi Y et al (2019) Towards smart healthcare management based on knowledge graph technology[C]. The 2019 8th International Conference

  18. Dong X, Chowdhury S, Qian L et al (2017) Transfer bi-directional lstm rnn for named entity recognition in Chinese electronic medical records[C], 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE pp 1–4

  19. Liu M, Zhang Y, Li W et al (2020) Joint model of entity recognition and relation extraction with self-attention mechanism[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). https://doi.org/10.1145/3387634

  20. Xue K, Zhou Y, Ma Z et al (2019) Fine-tuning BERT for joint entity and relation extraction in Chinese medical text. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 892–897

  21. Wei Q, Zhou Y, Zhao B et al (2020) Named entity recognition from table headers in randomized controlled trial articles[C]. 2020 IEEE International Conference on Healthcare Informatics (ICHI). IEEE

  22. Morales-Burton V, Lopez-Ramirez SA (2022) Rethinking healthcare quality and prestige: is this a manager's number one problem?[J]. LSE Research Online Documents on Economics

  23. Li X, Zhang H, Zhou XH (2020) Chinese clinical named entity recognition with variant neural structures based on BERT methods. J Biomed Inform 107:103422

    Article  PubMed  Google Scholar 

  24. Wen G, Chen H, Li H et al (2020) Cross domains adversarial learning for Chinese named entity recognition for online medical consultation. J Biomed Inform 112:103608

    Article  PubMed  Google Scholar 

  25. Li Z, Gan Z, Zhang B et al. Noisy label learning for Chinese medical named entity recognition based on uncertainty strategy. Proceedings of the Evaluation Tasks at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2020)

  26. Xue K, Zhou Y, Ma Z et al (2019) Fine-tuning BERT for joint entity and relation extraction in Chinese medical text[J]. IEEE. https://doi.org/10.1109/BIBM47256.2019.8983370

  27. Yin R, Wang Q, Li P et al (2016) Multi-granularity chinese word embedding[C]. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: 981–986

  28. Yin R, Wang Q, Li P et al (2016) Multi-granularity Chinese word embedding[C]//conference on empirical methods in natural language processing. https://doi.org/10.18653/v1/D16-1100

  29. Li Y, Wang X, Hui L et al (2020) Chinese clinical named entity recognition in electronic medical records: Development of a lattice long short-term memory model with contextualized character representations. JMIR Med Inform 8(9):e19848

    Article  PubMed  PubMed Central  Google Scholar 

  30. Landolsi MY, Hlaoua L, Romdhane LB (2022) infonnation extraction from e1ectronic medica1 documents state of the art and future research directions[J]. Knowl Inf Syst 65(2):463–516. https://doi.org/10.1007/s10115-022-01779-1

    Article  PubMed  PubMed Central  Google Scholar 

  31. Landolsi MY, Romdhane LB, Hlaoua L (2022) Medical named entity recognition using surrounding sequences matching. Procedia Comput Sci[J] 207:674–683

    Article  Google Scholar 

  32. Méndez M, Merayo MG, Núñez M (2023) Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model. Eng Appl Artif Intell[J] 121:106041

    Article  Google Scholar 

  33. Sui Y, Bu F, Hu Y et al (2022) Trigger-GNN: A trigger-based graph neural network for nested named entity recognition[J]. https://doi.org/10.1109/IJCNN55064.2022.9892555

  34. Hssayni EH, Joudar NE, Ettaouil M (2022) Localization and reduction of redundancy in CNN using L1-sparsity induction[J]. J Ambient Intell Human Comput: 1–13. https://doi.org/10.1007/s12652-022-04025-2

  35. Hssayni EH, Joudar N-E, Ettaouil M (2022) An adaptive Drop method for deep neural networks regularization: Estimation of DropConnect hyperparameter using generalization gap. Knowl Based Syst [J] 253:109567

    Article  Google Scholar 

  36. Hssayni Eh, Joudar N-E, Ettaouil M (2022) A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization. Comput Intell 38(6):2056–2074

    Article  Google Scholar 

  37. Cui Y, Che W, Liu T et al (2021) Pre-training with whole wordmasking for chinese bert[J]. IEEE/ACM Trans Audio Speech Lang Process 29:3504–3514

    Article  Google Scholar 

  38. Jin Y, Xiong Y, Shi D et al (2023) Learning from undercodedclinical records for automated International Classification of Diseases (ICD) coding[J]. J Am Med Inform Assoc 30(3):438–446

    Article  PubMed  Google Scholar 

  39. Li X, Sun Z, Zhu G (2023) CCRFs-NER: Named entity recognition method based on cascaded conditional random fields oriented Chinese EMR[C]. Tenth Inter-national Conference on Applications and Techniques in Cyber Intelli gence (ICATCI 2022) volume 2. Springer International Publishing, Cham, pp 229–237

  40. Cui S, Joe I (2023) A multi-head adjacent attention-based pyramidlayered model for nested named entity recognition[J]. Neural Comput Appl 35(3):2561–2574

    Article  Google Scholar 

  41. Yao L, Jiang MF, Fang X et al (2022) Research on chinese clinical named entity recognition method based on radical feature and BERT-Transformer-CRF[J]. Softw Eng 25(12):30–36. https://doi.org/10.19644/j.cnki.issn2096-1472.2022.012.007

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61872284); Industrial field of general projects of science and Technology Department of Shaanxi Province(2023-YBGY-203, 2023-YBGY-021); Industrialization Project of Shaanxi Provincial Department of Education (21JC017); "Thirteenth Five-Year" National Key R&D Program Project (Project Number: 2019YFD1100901). Natural Science Foundation of Shannxi Province, China(2021JLM-16).

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Authors and Affiliations

  1. School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, 710054, China

    Qinlu He, Pengze Gao, Fan Zhang & Genqing Bian

  2. Shaan Xi Institute of Metrology Science, Xi’an, 710043, China

    Zhen Li

  3. Shanghai Today Information Technology Co., Ltd, Shang Hai, 200080, China

    Zan Wang

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  1. Qinlu He
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  2. Pengze Gao
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  3. Fan Zhang
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  4. Genqing Bian
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  5. Zhen Li
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  6. Zan Wang
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Contributions

Qinlu He and Zhen Li contributed significantly to Design Algorithm. Genqing Bian and Zan Wang performed the perfor analysis. Pengze Gao completed the experimental part. Fan Zhang and Qinlu He contributed to manuscript preparation.

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Correspondence to Qinlu He or Zhen Li.

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He, Q., Gao, P., Zhang, F. et al. Healthcare entity recognition based on deep learning. Multimed Tools Appl 83, 32739–32763 (2024). https://doi.org/10.1007/s11042-023-16900-x

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