{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:17:33Z","timestamp":1775632653450,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T00:00:00Z","timestamp":1697241600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T00:00:00Z","timestamp":1697241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62205168"],"award-info":[{"award-number":["62205168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J01916"],"award-info":[{"award-number":["2020J01916"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J05109"],"award-info":[{"award-number":["2020J05109"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022J011166"],"award-info":[{"award-number":["2022J011166"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11517-023-02934-8","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T23:02:12Z","timestamp":1697238132000},"page":"327-341","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["MMBERT: a unified framework for biomedical named entity recognition"],"prefix":"10.1007","volume":"62","author":[{"given":"Lei","family":"Fu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1089-8673","authenticated-orcid":false,"given":"Zuquan","family":"Weng","sequence":"additional","affiliation":[]},{"given":"Jiheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Haihe","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yiqing","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,14]]},"reference":[{"key":"2934_CR1","first-page":"713","volume":"15","author":"H He","year":"2017","unstructured":"He H, Sun X (2017) F-score driven max margin neural network for named entity recognition in Chinese social media. EACL 15:713\u2013718","journal-title":"EACL"},{"key":"2934_CR2","first-page":"770","volume":"15","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. CVPR 15:770\u2013778","journal-title":"CVPR"},{"key":"2934_CR3","first-page":"6843","volume":"1","author":"F Hou","year":"2020","unstructured":"Hou F, Wang R, He J, Zhou Y (2020) Improving entity linking through semantic reinforced entity embeddings. ACL 1:6843\u20136848","journal-title":"ACL"},{"key":"2934_CR4","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0378-1119(00)00431-5","volume":"259","author":"M Krauthammer","year":"2000","unstructured":"Krauthammer M, Rzhetsky A, Morozov P et al (2000) Using BLAST for identifying gene and protein names in journal articles. Gene 259:245\u2013252","journal-title":"Gene"},{"key":"2934_CR5","first-page":"652","volume":"13","author":"R Leaman","year":"2008","unstructured":"Leaman R, Gonzalez G (2008) BANNER: an executable survey of advances in biomedical named entity recognition. Pacific Symposium Biocomputing 13:652\u2013663","journal-title":"Pacific Symposium Biocomputing"},{"key":"2934_CR6","doi-asserted-by":"crossref","unstructured":"Li Y, Lin H, Yang Z (2009) Incorporating rich background knowledge for gene named entity classification and recognition.BMC Bioinforma, 10:1\u201310","DOI":"10.1186\/1471-2105-10-223"},{"key":"2934_CR7","first-page":"71","volume":"15","author":"Z Huang","year":"2015","unstructured":"Huang Z, Wei X, Kai Y (2015) Bidirectional LSTM-CRF models for sequence tagging. EACL 15:71\u2013718","journal-title":"EACL"},{"key":"2934_CR8","first-page":"1554","volume":"56","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Yang J (2018) Chinese NER Using Lattice LSTM. ACL 56:1554\u20131564","journal-title":"Chinese NER Using Lattice LSTM. ACL"},{"issue":"31","key":"2934_CR9","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Advances in Neural Information Processing Systems 30(31):5998\u20136008","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2934_CR10","unstructured":"Devlin J, Chang M W, Lee K, et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, 1:4171\u20134186"},{"issue":"S2","key":"2934_CR11","first-page":"138","volume":"46","author":"W Ziniu","year":"2019","unstructured":"Ziniu W, Meng J, Jianling G et al (2019) Chinese named entity recognition method based on BERT. Comput Sci 46(S2):138\u2013142","journal-title":"Comput Sci"},{"key":"2934_CR12","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert R, Weston J, Bottou L et al (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493\u20132537","journal-title":"J Mach Learn Res"},{"key":"2934_CR13","doi-asserted-by":"crossref","unstructured":"Ma X, Hovy E (2016) End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th annual meeting of the association for computational linguistics, vol\u00a01, pp\u00a01064\u20131074","DOI":"10.18653\/v1\/P16-1101"},{"key":"2934_CR14","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JP Chiu","year":"2016","unstructured":"Chiu JP, Nichols E (2016) Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics 4:357\u2013370","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"2934_CR15","unstructured":"Rei M, Crichton G, Pyysalo S (2016) Attending to characters in neural sequence labeling models. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, vol\u00a01, pp\u00a0309\u2013318"},{"key":"2934_CR16","unstructured":"Tomas M, Kai C, Greg Corrado et al (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"2934_CR17","first-page":"359","volume":"30","author":"D Graziella","year":"2022","unstructured":"Graziella D, Gianvito P, Michelangelo C (2022) PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments. CVPR 30:359-390","journal-title":"CVPR"},{"key":"2934_CR18","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.inffus.2022.11.029","volume":"92","author":"P Antonio","year":"2023","unstructured":"Antonio P, Gianvito P, Michelangelo C (2023) SAIRUS: spatially-aware identification of risky users in social networks. Information Fusion 92:435\u2013449","journal-title":"Information Fusion"},{"key":"2934_CR19","first-page":"1188","volume":"32","author":"Q Le","year":"2014","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. International Conference on Machine Learning 32:1188\u20131196","journal-title":"International Conference on Machine Learning"},{"issue":"8","key":"2934_CR20","first-page":"3199","volume":"8","author":"D Jenish","year":"2022","unstructured":"Jenish D, Rupa M, Dipti R (2022) Effective and scalable legal judgment recommendation using pre-learned word embedding. Complex & Intelligent Systems 8(8):3199\u20133213","journal-title":"Complex & Intelligent Systems"},{"key":"2934_CR21","unstructured":"Yiming C, Wanxiang C, Ting L, et al (2020) Revisiting pre-trained models for chinese natural language processing. In: Findings of the association for computational linguistics: EMNLP 2020, vol\u00a01, pp\u00a0657\u2013668"},{"key":"2934_CR22","doi-asserted-by":"crossref","unstructured":"Li X, Yan H, Qiu X, et al (2020) FLAT: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th annual meeting of the association for computational linguistics, vol\u00a01, pp\u00a06836\u20136842","DOI":"10.18653\/v1\/2020.acl-main.611"},{"key":"2934_CR23","unstructured":"Burr S (2004) Biomedical named entity recognition using conditional random fields and rich feature set, 107-110. In: Proceedings of association for computational linguistics. Barcelona, Spain"},{"issue":"5","key":"2934_CR24","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1136\/amiajnl-2011-000164","volume":"18","author":"C Clark","year":"2010","unstructured":"Clark C, Aberdeen J, Coarr M et al (2010) MITRE system for clinical assertion status classification. J Am Med Inform Assoc 18(5):563\u2013567","journal-title":"J Am Med Inform Assoc"},{"key":"2934_CR25","first-page":"355","volume":"639","author":"K Xu","year":"2017","unstructured":"Xu K, Zhou Z, Hao T et al (2017) A bidirectional LSTM and conditional random fields approach to medical named entity recognition. Proceedings of international conference on advanced intelligent systems and informatics 639:355\u2013365","journal-title":"Proceedings of international conference on advanced intelligent systems and informatics"},{"key":"2934_CR26","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.neunet.2019.08.032","volume":"121","author":"L Gligic","year":"2020","unstructured":"Gligic L, Kormilitzin A, Goldberg P et al (2020) Named entity recognition in electronic health records using transfer learning bootstrapped neural networks. Neural Netw 121:132\u2013139","journal-title":"Neural Netw"},{"key":"2934_CR27","unstructured":"Wang Y, Liu Y, Yu Z, et al (2012) A preliminary work on symptom name recognition from free-text clinical records of traditional Chinese medicine using conditional random fields and reasonable features, pp\u00a0223\u2013230. Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, Montreal, Canada"},{"key":"2934_CR28","doi-asserted-by":"crossref","unstructured":"Liu K, Hu Q, Liu J (2017) Named entity recognition in Chinese electronic medical records based on CRF, pp\u00a0107-110. In: Proceedings of 14th web information systems and applications conference (WISA 2017), Guangxi, China","DOI":"10.1109\/WISA.2017.8"},{"issue":"1","key":"2934_CR29","first-page":"1","volume":"52","author":"S Ya","year":"2016","unstructured":"Ya S, Jie L, Yalou H (2016) Entity recognition research in online medical texts. Journal of Peking University (Natural Science Edition) 52(1):1\u20139","journal-title":"Journal of Peking University (Natural Science Edition)"},{"issue":"1","key":"2934_CR30","first-page":"123","volume":"36","author":"Z Fan","year":"2017","unstructured":"Fan Z, Min W (2017) Medical text entities recognition method base on deep learning. Computing Technology and Automation 36(1):123\u2013127","journal-title":"Computing Technology and Automation"},{"key":"2934_CR31","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, Xiaosheng Y et al (2022) Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT. BMC Medical Informatics and Decision Making 22:315","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"2","key":"2934_CR32","first-page":"8","volume":"28","author":"Y Wenming","year":"2019","unstructured":"Wenming Y, Weijie C (2019) Named entity recognition of online medical question answering text. Comput Syst 28(2):8\u201314","journal-title":"Comput Syst"},{"issue":"3","key":"2934_CR33","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s12911-019-0787-y","volume":"19","author":"B Tang","year":"2019","unstructured":"Tang B, Wang X, Yan J (2019) Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF. BMC Medical Informatics and Decision Making 19(3):74\u201382","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"5","key":"2934_CR34","first-page":"497","volume":"40","author":"P Cuiran","year":"2019","unstructured":"Cuiran P, Qinghua W, Buzhou T et al (2019) Chinese electronic medical record named entity recognition based on sentence-level Lattice-lona short-term memory neural network. Journal of the Second Military Medical University 40(5):497\u2013506","journal-title":"Journal of the Second Military Medical University"},{"issue":"5","key":"2934_CR35","first-page":"153","volume":"56","author":"L Bo","year":"2020","unstructured":"Bo L, Xiaodong K, Huali Z et al (2020) Named entity recognition in Chinese electronic medical records using transformer-CRF. Computer Engineering and Applications 56(5):153\u2013159","journal-title":"Computer Engineering and Applications"},{"issue":"10","key":"2934_CR36","first-page":"1943","volume":"43","author":"L Ling","year":"2020","unstructured":"Ling L, Zhihao Y, Yawen S et al (2020) Chinese clinical named entity recognition based on stroke ELMo and multi-task learning. J Comput 43(10):1943\u20131957","journal-title":"J Comput"},{"issue":"3","key":"2934_CR37","first-page":"211","volume":"47","author":"T Guoqiang","year":"2020","unstructured":"Guoqiang T, Daqi G, Tong R et al (2020) Clinical electronic medical record named entity recognition incorporating language model. Comput Sci 47(3):211\u2013216","journal-title":"Comput Sci"},{"issue":"8","key":"2934_CR38","first-page":"97","volume":"11","author":"S Zhoufeng","year":"2021","unstructured":"Zhoufeng S, Qianmin S, Jinglei G (2021) Named entity recognition model of Chinese clinical electronic medical record based on XLNet-BiLSTM. Intelligent Computer and Applications 11(8):97\u2013102","journal-title":"Intelligent Computer and Applications"},{"issue":"3","key":"2934_CR39","first-page":"159","volume":"38","author":"Z Qingxia","year":"2021","unstructured":"Qingxia Z, Wangping X, Jianqiang D et al (2021) Electronic medical record named entity recognition combined with self-attention BiLSTM-CRF. Computer Applications and Software 38(3):159\u2013162","journal-title":"Computer Applications and Software"},{"key":"2934_CR40","first-page":"51","volume":"2","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Zhang L, Wang Y (2021) Named entity recognition on Chinese electronic medical records based on RoBERTa-WWM. Computer and Modernization 2:51\u201355","journal-title":"Computer and Modernization"},{"key":"2934_CR41","unstructured":"He T, Chen J, Wen Y (2022) Research on entity recognition of electronic medical record based on BERT-CRF Model. Computer and Digital Engineering 50(3):639\u2013643"},{"key":"2934_CR42","unstructured":"Jingye L, Hao F, Jiang L, et al (2022) Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI 2022 conference on artificial intelligence, 36(1):1\u20139"},{"issue":"10","key":"2934_CR43","first-page":"10965","volume":"36","author":"X Li","year":"2020","unstructured":"Li X, Yan H, Qiu X et al (2020) FLAT: Chinese NER using flat-lattice transformer. ACL 36(10):10965\u2013109721","journal-title":"ACL"},{"key":"2934_CR44","unstructured":"Ningyu Z, Shumin D, Zhen B, et al (2021) ERNIE-Health: a pre-trained language model for Chinese biomedical text understanding, arXiv:2110.07244"},{"key":"2934_CR45","unstructured":"Ilias C, Manos F, Prodromos M, et al (2020) LEGAL-BERT: the muppets straight out of law school. In: Findings of the association for computational linguistics: EMNLP 2020 findings-emnlp, vol\u00a0261, pp\u00a02898\u20132904"},{"issue":"1","key":"2934_CR46","first-page":"1","volume":"35","author":"Z Liu","year":"2022","unstructured":"Liu Z, Mao H, Wu CY et al (2022) A ConvNet for the 2020s. CVPR 35(1):1\u201314","journal-title":"CVPR"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02934-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02934-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02934-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T15:32:59Z","timestamp":1704209579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02934-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,14]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["2934"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02934-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,14]]},"assertion":[{"value":"9 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 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 procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing of interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}