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It is of great significance for follow\u2010up clinical studies. Most of the existing CNER methods fail to give enough thought to Chinese radical\u2010level characteristics and the specialty of the Chinese field. This paper proposes the Ra\u2010RC model, which combines radical features and a deep learning structure to fix this problem. A bidirectional encoder representation of transformer (RoBERTa) is utilized to learn medical features thoroughly. Simultaneously, we use the bidirectional long short\u2010term memory (BiLSTM) network to extract radical\u2010level information to capture the internal relevance of characteristics and stitch the eigenvectors generated by RoBERTa. In addition, the relationship between labels is considered to obtain the optimal tag sequence by applying conditional random field (CRF). The experimental results demonstrate that the proposed Ra\u2010RC model achieves F1 score 93.26% and 82.87% on the CCKS2017 and CCKS2019 datasets, respectively.<\/jats:p>","DOI":"10.1155\/2021\/2489754","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T22:37:41Z","timestamp":1624919861000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Research on Named Entity Recognition of Electronic Medical Records Based on RoBERTa and Radical\u2010Level Feature"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6786-9630","authenticated-orcid":false,"given":"Yue","family":"Wu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9717-8355","authenticated-orcid":false,"given":"Jie","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Caie","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3845-6612","authenticated-orcid":false,"given":"Huilin","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9882-3029","authenticated-orcid":false,"given":"Jian","family":"Wan","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3039500"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2020.3027681"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-019-01246-2"},{"key":"e_1_2_9_4_2","first-page":"1943","article-title":"Chinese clinical named entity recognition based on stroke ELMo and multi-task learning","volume":"43","author":"Luo L.","year":"2020","journal-title":"Chinese Journal of Computers"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-15-S1-S9"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2008.12.005"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"LiD. 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