Abstract
Medical institutions commonly utilize electronic medical records (EMRs) to document patients’ medical conditions, which contain valuable medical information. However, EMRs often consist of semi-structured or unstructured data, presenting significant challenges in processing and analysis. This paper addresses the needs of subsequent tasks such as assisting diagnosis and information extraction (IE). We present the process of structuring electronic health records based on pediatric epilepsy cases, including EMRs structural analysis, data preprocessing, named entity recognition (NER), and data integration. NER is a critical step in the processing pipeline. Therefore, we propose a pediatric epilepsy EMRs entity recognition model, T-RoBERTa-BiLSTM-CRF, based on transfer learning. After training on the constructed pediatric epilepsy dataset, T-RoBERTa-BiLSTM-CRF is used to perform NER tasks in EMRs structuring, achieving an F1 score of 79.25\(\%\). Identified medical entities are integrated at the text level to achieve EMRs structuring aligned with the needs of subsequent tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hongying, Z., Wenxin, L., Kunli, Z., Yajuan, Y., Baobao, C., Zhifang, S.: Building a pediatric medical corpus: word segmentation and named entity annotation. In: Chinese Lexical Semantics: 21st Workshop, CLSW 2020, Hong Kong, China, 28–30 May 2020, Revised Selected Papers 21, pp. 652–664. Springer (2021). https://doi.org/10.1007/978-3-030-81197-6-55
Chowdhury, S., et al.: A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records. BMC Bioinformatics 19, 75–84 (2018). https://doi.org/10.1186/s12859-018-2467-9
Yu, X., Hu, W., Lu, S., Sun, X., Yuan, Z.: Biobert based named entity recognition in electronic medical record. In: 2019 10th International Conference on Information Technology in Medicine and Education (ITME), pp. 49–52. IEEE (2019). https://doi.org/10.1109/ITME.2019.00022
An, Y., Xia, X., Chen, X., Wu, F.X., Wang, J.: Chinese clinical named entity recognition via multi-head self-attention based bilstm-CRF. Artif. Intell. Med. 127, 102282 (2022). https://doi.org/10.1016/j.artmed.2022.102282
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6
Zhang, K., Yue, D., Zhuang, L.: Improving Chinese clinical named entity recognition based on bilstm-CRF by cross-domain transfer. In: Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence, pp. 251–256 (2020). https://doi.org/10.1145/3409501.3409527
Wang, Z., et al.: Label-aware double transfer learning for cross-specialty medical named entity recognition. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1–15 (2018). https://doi.org/10.18653/v1/N18-1001
Li, X.: Ministry of health issues basic specifications for electronic medical records. Chin. Commun. Phys. 13, 1–21 (2010). (in Chinese)
Zhao, Y.S., Zhang, K.L., Ma, H.C., Li, K.: Leveraging text skeleton for de-identification of electronic medical records. BMC Med. Inform. Decis. Mak. 18, 65–72 (2018). https://doi.org/10.1186/s12911-018-0598-6
Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).
Zhang, N., et al.: Cblue: a chinese biomedical language understanding evaluation benchmark. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers, pp. 7888–7915 (2022). https://doi.org/10.18653/v1/2022.acl-long.544
Lin, B.Y., Lu, W.: Neural adaptation layers for cross-domain named entity recognition. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2012–2022 (2018). https://doi.org/10.18653/v1/D18-1226
Acknowledgement
We appreciate the constructive feedback from the anonymous reviewers and the support provided for this research by the following projects: Yunnan Province Science and Technology Major Project (202102AA100021), and Henan Provincial Department of Science and Technology Science and Technology Tackling Project (222102210231).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Song, Y. et al. (2024). Research on the Structure of Pediatric Epilepsy Electronic Medical Records Based on Transfer Learning. In: Dong, M., Hong, JF., Lin, J., Jin, P. (eds) Chinese Lexical Semantics. CLSW 2023. Lecture Notes in Computer Science(), vol 14515. Springer, Singapore. https://doi.org/10.1007/978-981-97-0586-3_7
Download citation
DOI: https://doi.org/10.1007/978-981-97-0586-3_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0585-6
Online ISBN: 978-981-97-0586-3
eBook Packages: Computer ScienceComputer Science (R0)