Research on the Structure of Pediatric Epilepsy Electronic Medical Records Based on Transfer Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0586-3_7
Reference12 articles.
1. 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
2. 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
3. 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
4. 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
5. 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
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