{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T21:38:42Z","timestamp":1763156322072},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T00:00:00Z","timestamp":1689379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T00:00:00Z","timestamp":1689379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11517-023-02871-6","type":"journal-article","created":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T16:01:46Z","timestamp":1689436906000},"page":"2733-2743","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A weakly supervised method for named entity recognition of Chinese electronic medical records"],"prefix":"10.1007","volume":"61","author":[{"given":"Meng","family":"Li","sequence":"first","affiliation":[]},{"given":"Chunrong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Kuang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huajian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Ying","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,15]]},"reference":[{"key":"2871_CR1","doi-asserted-by":"crossref","unstructured":"Chuanhai D, Jiajun Z, Chengqing Z et al (2016) Character based LSTM-CRF with radical-level features for Chinese named entity recognition[C]\/\/Natural Language Understanding and Intelligent Applications - 5th Conference on Natural Language Processing and Chinese Computing( NLPCC). Kunming: Springer Press, 239-250","DOI":"10.1007\/978-3-319-50496-4_20"},{"key":"2871_CR2","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s11517-015-1322-7","volume":"54","author":"J Heurix","year":"2016","unstructured":"Heurix J, Fenz S, Rella A et al (2016) Recognition and pseudonymisation of medical records for secondary use. Med Biol Eng Comput 54:371\u2013383. https:\/\/doi.org\/10.1007\/s11517-015-1322-7","journal-title":"Med Biol Eng Comput"},{"key":"2871_CR3","unstructured":"Feng Y, Sun L, Zhang J (2005) Early results for Chinese named entity recognition using conditional random fields model, HMM and maximum entropy [C]\/\/International Conference on Natural Language Processing and Knowledge Engineering.Wuhan,China: IEEE Press, 549\u2013552"},{"key":"2871_CR4","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s11517-021-02359-1","volume":"59","author":"X Cen","year":"2021","unstructured":"Cen X, Yuan J, Pan C et al (2021) Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records. Med Biol Eng Comput 59:1111\u20131121. https:\/\/doi.org\/10.1007\/s11517-021-02359-1","journal-title":"Med Biol Eng Comput"},{"issue":"4","key":"2871_CR5","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1093\/jamia\/ocw180","volume":"24","author":"A Cocos","year":"2017","unstructured":"Cocos A, Fiks AG, Masino AJ (2017) Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts[J]. J Am Med Inform Assoc: JAMIA 24(4):813\u2013821. https:\/\/doi.org\/10.1093\/jamia\/ocw180","journal-title":"J Am Med Inform Assoc: JAMIA"},{"key":"2871_CR6","doi-asserted-by":"publisher","unstructured":"Cho K,Van Merrienboer B,Gulcehre C et al (2014) Learning phrase representations using RNN encoder- decoder for statistical machine translation[J]. arXiv:1406.1078. https:\/\/doi.org\/10.48550\/arXiv.1406.1078","DOI":"10.48550\/arXiv.1406.1078"},{"issue":"8","key":"2871_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory[J]. Neural Computation 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Computation"},{"key":"2871_CR8","doi-asserted-by":"crossref","unstructured":"Kim J, Woodland P C (2000) A rule-based named entity recognition system for speech input [C]\/\/Proceedings of the 6th International Conference on Spoken Language Processing, Beijing, China, 528\u2013531","DOI":"10.21437\/ICSLP.2000-131"},{"key":"2871_CR9","doi-asserted-by":"crossref","unstructured":"Asahara M, Matsumoto Y (2003) Japanese named entity extraction with redundant morphological analysis[C]\/\/Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1:8-15","DOI":"10.3115\/1073445.1073447"},{"key":"2871_CR10","unstructured":"Chen L, Yue Y, Haoming J et al (2020) BOND: bert-assisted open-domain named entity recognition with distant supervision. In KDD \u201920: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23\u201327, 1054\u20131064"},{"key":"2871_CR11","doi-asserted-by":"publisher","unstructured":"Lample G, Ballesteros M, Subramanian S et al (2016) Neural architectures for named entity recognition[J]. arXiv:1603.01360. https:\/\/doi.org\/10.48550\/arXiv.1603.01360","DOI":"10.48550\/arXiv.1603.01360"},{"issue":"3","key":"2871_CR12","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1162\/dint_a_00093","volume":"3","author":"L Xia","year":"2021","unstructured":"Xia L, Qinghua W, Hu L et al (2021) (2021) Overview of CCKS 2020 Task 3: named entity recognition and event extraction in Chinese electronic medical records. Data Intell 3(3):376\u2013388. https:\/\/doi.org\/10.1162\/dint_a_00093","journal-title":"Data Intell"},{"key":"2871_CR13","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1186\/s12911-022-02059-2","volume":"22","author":"P Chen","year":"2022","unstructured":"Chen P, Zhang M, Yu X et al (2022) Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT. BMC Med Inform Decis Mak 22:315. https:\/\/doi.org\/10.1186\/s12911-022-02059-2","journal-title":"BMC Med Inform Decis Mak"},{"key":"2871_CR14","first-page":"624","volume":"216","author":"Y Wu","year":"2015","unstructured":"Wu Y, Jiang M, Lei J et al (2015) Named entity recognition in Chinese clinical text using deep neural network[J]. Stud Health Technol Inform 216:624","journal-title":"Stud Health Technol Inform"},{"key":"2871_CR15","unstructured":"Xiaonan L, Yan H, Xipeng Q, Xuanjing H (2020) FLAT: Chinese NER using flat-lattice transformer[C]\/\/Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ACL, 6836\u20136842"},{"key":"2871_CR16","unstructured":"Yangtian Y, Xinyu Z, Xian W (2020) Medical named entity recognition based on BERT and glyphs[C]\/\/Proceedings of the Evaluation Task at the China Conference on Knowledge Graph and Cognitive Intelligence. Nanchang: CCKS"},{"key":"2871_CR17","doi-asserted-by":"publisher","unstructured":"Gong L, Zhang Z, Chen S (2020) Clinical named entity recognition from Chinese electronic medical records based on deep learning pretraining [J]. J Healthc Eng 8829219. https:\/\/doi.org\/10.1155\/2020\/8829219","DOI":"10.1155\/2020\/8829219"},{"issue":"2\/3","key":"2871_CR18","doi-asserted-by":"publisher","first-page":"251","DOI":"10.11925\/infotech.2096-3467.2021.0910","volume":"6","author":"Z Fangcong","year":"2022","unstructured":"Fangcong Z, Qiuli Q, Yong J, Runtao Z (2022) Named entity recognition for Chinese EMR with RoBERTa-WWM-BiLSTM-CRF[J]. Data Anal Knowl Discov 6(2\/3):251\u2013262. https:\/\/doi.org\/10.11925\/infotech.2096-3467.2021.0910","journal-title":"Data Anal Knowl Discov"},{"issue":"6","key":"2871_CR19","doi-asserted-by":"publisher","first-page":"105","DOI":"10.13365\/j.jirm.2021.06.105","volume":"11","author":"J Shenqi","year":"2021","unstructured":"Shenqi J, Youlin Z (2021) Recognizing clinical named entity from Chinese electronic medical record texts based on semi-supervised deep learning[J]. J Inf Resour Manag 11(6):105\u2013115. https:\/\/doi.org\/10.13365\/j.jirm.2021.06.105","journal-title":"J Inf Resour Manag"},{"key":"2871_CR20","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1007\/s12559-022-10003-9","volume":"14","author":"LL Ma","year":"2022","unstructured":"Ma LL, Yang J, An B et al (2022) Medical named entity recognition using weakly supervised learning. Cogn Comput 14:1068\u20131079. https:\/\/doi.org\/10.1007\/s12559-022-10003-9","journal-title":"Cogn Comput"},{"key":"2871_CR21","doi-asserted-by":"publisher","unstructured":"Duan Y, Ma LL, Han X et al (2020) External knowledge-based weakly supervised learning approach on Chinese clinical named entity recognition [J]. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Lecture Notes in Computer Science, vol 12032. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-41407-8_22","DOI":"10.1007\/978-3-030-41407-8_22"},{"key":"2871_CR22","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M W, Lee K et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding [J]. arXiv:1810.04805. https:\/\/doi.org\/10.48550\/arXiv.1810.04805","DOI":"10.48550\/arXiv.1810.04805"},{"key":"2871_CR23","doi-asserted-by":"publisher","unstructured":"Zhang N, Jia Q, Yin K et al (2020) Conceptualized representation learning for Chinese biomedical text mining[J]. arXiv:2008.10813. https:\/\/doi.org\/10.48550\/arXiv.2008.10813","DOI":"10.48550\/arXiv.2008.10813"},{"key":"2871_CR24","doi-asserted-by":"publisher","unstructured":"Kipf T N, Welling M (2016) Semi-supervised classification with graph convolutional networks[J]. arXiv:1609.02907. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"issue":"4","key":"2871_CR25","doi-asserted-by":"publisher","first-page":"e50","DOI":"10.2196\/medinform.9965","volume":"6","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Wang X, Hou Z, Li J (2018) Clinical named entity recognition from Chinese electronic health records via machine learning methods [J]. JMIR Med Inform 6(4):e50. https:\/\/doi.org\/10.2196\/medinform.9965","journal-title":"JMIR Med Inform"},{"key":"2871_CR26","unstructured":"Bianca Z and Charles E (2001) Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers[C]\/\/In Proceedings of the Eighteenth International Conference on Machine Learning (ICML '01). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 609\u2013616"},{"key":"2871_CR27","doi-asserted-by":"publisher","unstructured":"Du J, Grave E , Gunel B et al (2020) Self-training improves pre-training for natural language understanding [J]. arXiv:2010.02194. https:\/\/doi.org\/10.48550\/arXiv.2010.02194","DOI":"10.48550\/arXiv.2010.02194"},{"key":"2871_CR28","unstructured":"Rui Q, Xiaoran Y, Wenkang H (2019) Medical named entity recognition based on BERT and model fusion [C]. Evaluation Paper of 2019 National Knowledge Graph and Semantic Computing Conference, CCKS 2019"},{"key":"2871_CR29","unstructured":"Minglu L, Xuesi Z, Zheng C et al (2019) Team MSIIP at CCKS 2019 Task 1[C]. Evaluation Paper of 2019 National Knowledge Graph and Semantic Computing Conference, CCKS 2019"},{"key":"2871_CR30","unstructured":"Li N, Luo L, Ding Z et al (2019) DUTIR at the CCKS-2019 Task 1: improving Chinese clinical named entity recognition using stroke ELMo and transfer learning [C]. Evaluation Paper of 2019 National Knowledge Graph and Semantic Computing Conference, CCKS 2019"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02871-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02871-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02871-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T05:15:00Z","timestamp":1695791700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02871-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,15]]},"references-count":30,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["2871"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02871-6","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,15]]},"assertion":[{"value":"24 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All electronic medical record data used in this study have undergone de-identification procedures to protect sensitive information in compliance with the ethical guidelines and principles of the relevant institutions involved in the collection and use of the electronic medical record data.This research solely focuses on the study of electronic medical record text, and not on the diseases themselves. Therefore, ethical approval is not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}