{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:13:42Z","timestamp":1780676022115,"version":"3.54.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Science and Technology Innovation 2030 - Major Project","award":["2020AAA0104902"],"award-info":[{"award-number":["2020AAA0104902"]}]},{"name":"Science and Technology Innovation 2030 - Major Project","award":["2020AAA0104902"],"award-info":[{"award-number":["2020AAA0104902"]}]},{"name":"Science and Technology Innovation 2030 - Major Project","award":["2020AAA0104902"],"award-info":[{"award-number":["2020AAA0104902"]}]},{"name":"Science and Technology Innovation 2030 - Major Project","award":["2020AAA0104902"],"award-info":[{"award-number":["2020AAA0104902"]}]},{"DOI":"10.13039\/501100019018","name":"CAMS Initiative for Innovative Medicine","doi-asserted-by":"crossref","award":["2017-I2M-3-014"],"award-info":[{"award-number":["2017-I2M-3-014"]}],"id":[{"id":"10.13039\/501100019018","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019018","name":"CAMS Initiative for Innovative Medicine","doi-asserted-by":"crossref","award":["2017-I2M-3-014"],"award-info":[{"award-number":["2017-I2M-3-014"]}],"id":[{"id":"10.13039\/501100019018","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019018","name":"CAMS Initiative for Innovative Medicine","doi-asserted-by":"crossref","award":["2017-I2M-3-014"],"award-info":[{"award-number":["2017-I2M-3-014"]}],"id":[{"id":"10.13039\/501100019018","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019018","name":"CAMS Initiative for Innovative Medicine","doi-asserted-by":"crossref","award":["2017-I2M-3-014"],"award-info":[{"award-number":["2017-I2M-3-014"]}],"id":[{"id":"10.13039\/501100019018","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019018","name":"CAMS Initiative for Innovative Medicine","doi-asserted-by":"crossref","award":["2017-I2M-3-014"],"award-info":[{"award-number":["2017-I2M-3-014"]}],"id":[{"id":"10.13039\/501100019018","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0901705"],"award-info":[{"award-number":["2016YFC0901705"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient\u2019s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01810-z","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T12:06:02Z","timestamp":1648037162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records"],"prefix":"10.1186","volume":"22","author":[{"given":"An","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanqing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Lou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiling","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianlai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"issue":"6","key":"1810_CR1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.2217\/cns.15.21","volume":"4","author":"D Theodros","year":"2015","unstructured":"Theodros D, Patel M, Ruzevick J, Lim M, Bettegowda C. Pituitary adenomas: historical perspective, surgical management and future directions. CNS Oncol. 2015;4(6):411\u201329. https:\/\/doi.org\/10.2217\/cns.15.21.","journal-title":"CNS Oncol"},{"key":"1810_CR2","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3389\/fendo.2015.00097","volume":"6","author":"LV Syro","year":"2015","unstructured":"Syro LV, Rotondo F, Ramirez A, et al. Progress in the diagnosis and classification of pituitary adenomas. Front Endocrinol (Lausanne). 2015;6:97. https:\/\/doi.org\/10.3389\/fendo.2015.00097.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"4","key":"1810_CR3","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/s11102-019-00960-0","volume":"22","author":"D Esposito","year":"2019","unstructured":"Esposito D, Olsson DS, Ragnarsson O, Buchfelder M, Skoglund T, Johannsson G. Non-functioning pituitary adenomas: indications for pituitary surgery and post-surgical management. Pituitary. 2019;22(4):422\u201334. https:\/\/doi.org\/10.1007\/s11102-019-00960-0.","journal-title":"Pituitary"},{"issue":"4","key":"1810_CR4","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1530\/EJE-14-0144","volume":"171","author":"A Tjornstrand","year":"2014","unstructured":"Tjornstrand A, Gunnarsson K, Evert M, Holmberg E, Ragnarsson O, Rosen T, Filipsson NH. The incidence rate of pituitary adenomas in western Sweden for the period 2001\u20132011. Eur J Endocrinol. 2014;171(4):519\u201326.","journal-title":"Eur J Endocrinol"},{"issue":"2","key":"1810_CR5","doi-asserted-by":"publisher","DOI":"10.2196\/12239","volume":"7","author":"S Sheikhalishahi","year":"2019","unstructured":"Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inform. 2019;7(2): e12239. https:\/\/doi.org\/10.2196\/12239.","journal-title":"JMIR Med Inform"},{"issue":"4","key":"1810_CR6","doi-asserted-by":"publisher","DOI":"10.2196\/14782","volume":"7","author":"H Wu","year":"2019","unstructured":"Wu H, Hodgson K, Dyson S, et al. Efficient reuse of natural language processing models for phenotype-mention identification in free-text electronic medical records: a phenotype embedding approach. JMIR Med Inform. 2019;7(4): e14782. https:\/\/doi.org\/10.2196\/14782.","journal-title":"JMIR Med Inform"},{"issue":"2","key":"1810_CR7","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1055\/s-0039-1681074","volume":"10","author":"PC Wei","year":"2019","unstructured":"Wei PC, Atalag K, Day K. An openEHR approach to detailed clinical model development: tobacco smoking summary archetype as a case study. Appl Clin Inform. 2019;10(2):219\u201328. https:\/\/doi.org\/10.1055\/s-0039-1681074.","journal-title":"Appl Clin Inform"},{"issue":"4","key":"1810_CR8","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1093\/jamia\/ocy173","volume":"26","author":"TA Koleck","year":"2019","unstructured":"Koleck TA, Dreisbach C, Bourne PE, Bakken S. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inform Assoc. 2019;26(4):364\u201379. https:\/\/doi.org\/10.1093\/jamia\/ocy173.","journal-title":"J Am Med Inform Assoc"},{"issue":"5","key":"1810_CR9","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/j.jbi.2009.07.007","volume":"42","author":"A Mykowiecka","year":"2009","unstructured":"Mykowiecka A, Marciniak M, Kup\u015b\u0107 A. Rule-based information extraction from patients\u2019 clinical data. J Biomed Inform. 2009;42(5):923\u201336.","journal-title":"J Biomed Inform"},{"issue":"1","key":"1810_CR10","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1186\/s12911-019-0894-9","volume":"19","author":"JS Obeid","year":"2019","unstructured":"Obeid JS, Weeda ER, Matuskowitz AJ, et al. Automated detection of altered mental status in emergency department clinical notes: a deep learning approach. BMC Med Inform Decis Mak. 2019;19(1):164. https:\/\/doi.org\/10.1186\/s12911-019-0894-9.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1810_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2019.01.007","volume":"172","author":"Su Jia","year":"2019","unstructured":"Jia Su, Jinpeng Hu, Jiang J, Xie J, Yang Y, He B, Yang J, Guan Yi. Extraction of risk factors for cardiovascular diseases from Chinese electronic medical records. Comput Methods Programs Biomed. 2019;172:1\u201310.","journal-title":"Comput Methods Programs Biomed"},{"key":"1810_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.103985","volume":"132","author":"X Zhang","year":"2019","unstructured":"Zhang X, Zhang Y, Zhang Q, Ren Y, Qiu T, Ma J, Sun Q. Extracting comprehensive clinical information for breast cancer using deep learning methods. Int J Med Inform. 2019;132: 103985.","journal-title":"Int J Med Inform"},{"issue":"1","key":"1810_CR13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1186\/s12911-020-01159-1","volume":"20","author":"Z Niazkhani","year":"2020","unstructured":"Niazkhani Z, Toni E, Cheshmekaboodi M, Georgiou A, Pirnejad H. Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: a systematic review. BMC Med Inform Decis Mak. 2020;20(1):153.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1810_CR14","unstructured":"Stubbs A, Uzuner O, Kumar V, Shaw S. Annotation guidelines: risk factors for heart disease in diabetic patients. i2b2\/UTHealth NLP. Challenge. 2014; 1\u20139."},{"issue":"1","key":"1810_CR15","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1197\/jamia.M2408","volume":"15","author":"O Uzuner","year":"2008","unstructured":"Uzuner O, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc. 2008;15(1):14\u201324.","journal-title":"J Am Med Inform Assoc"},{"key":"1810_CR16","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1136\/jamia.2010.003939","volume":"17","author":"J Patrick","year":"2010","unstructured":"Patrick J, Li M. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. J Am Med Inform Assoc. 2010;17:524\u20137.","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"1810_CR17","doi-asserted-by":"publisher","DOI":"10.2196\/16816","volume":"22","author":"J Wang","year":"2020","unstructured":"Wang J, Deng H, Liu B, et al. Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: bibliometric study on PubMed. J Med Internet Res. 2020;22(1): e16816.","journal-title":"J Med Internet Res"},{"issue":"4","key":"1810_CR18","doi-asserted-by":"publisher","DOI":"10.2196\/medinform.9965","volume":"6","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Wang X, Hou Z, Li J. Clinical named entity recognition from Chinese electronic health records via machine learning methods. JMIR Med Inform. 2018;6(4): e50.","journal-title":"JMIR Med Inform"},{"key":"1810_CR19","unstructured":"Hu J, Liu Z, Wang X, Chen Q, Tang B. A hybrid system for entity recognition from Chinese clinical text. In: Proceedings of the Evaluation Task at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2017), 26\u201329 August, 2017, Chengdu, China, 2017."},{"key":"1810_CR20","unstructured":"Si Y, Roberts K. A frame-based NLP system for cancer-related information extraction. In: AMIA annual symposium proceedings 2018, pp 1524\u201333."},{"issue":"8","key":"1810_CR21","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.3390\/ijerph17082687","volume":"17","author":"X Chen","year":"2020","unstructured":"Chen X, Ouyang C, Liu Y, Bu Y. Improving the named entity recognition of Chinese electronic medical records by combining domain dictionary and rules. Int J Environ Res Public Health. 2020;17(8):2687. https:\/\/doi.org\/10.3390\/ijerph17082687.","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"1810_CR22","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1186\/s12911-019-0865-1","volume":"19","author":"W Lee","year":"2019","unstructured":"Lee W, Choi J. Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition. BMC Med Inform Decis Mak. 2019;19(1):132. https:\/\/doi.org\/10.1186\/s12911-019-0865-1.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1810_CR23","unstructured":"Ling Y, Hasan SA, Farri O, et al. A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition. In: AMIA summits on translational science proceedings 2019, pp 761\u201370."},{"issue":"4","key":"1810_CR24","doi-asserted-by":"publisher","DOI":"10.2196\/14850","volume":"7","author":"M Jiang","year":"2019","unstructured":"Jiang M, Sanger T, Liu X. Combining contextualized embeddings and prior knowledge for clinical named entity recognition: evaluation study. JMIR Med Inform. 2019;7(4): e14850.","journal-title":"JMIR Med Inform"},{"key":"1810_CR25","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. Bert pre-training of deep bidirectional transformers for language understanding. arXiv Preprint arXiv:1810.04805 (2018)."},{"issue":"4","key":"1810_CR26","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1162\/coli.07-034-R2","volume":"34","author":"R Artstein","year":"2008","unstructured":"Artstein R, Poesio M. Inter-coder agreement for computational linguistics. Comput Lingusist. 2008;34(4):555\u201396.","journal-title":"Comput Lingusist"},{"key":"1810_CR27","unstructured":"Liu W, Xu T, Xu Q, et al. An encoding strategy based word-character LSTM for Chinese NER [C]. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, Vol. 1 (Long and Short Papers). 2019; p. 2379\u20132389."},{"key":"1810_CR28","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J. Chinese ner using lattice lstm [J]. arXiv preprint arXiv:1805.02023. 2018.","DOI":"10.18653\/v1\/P18-1144"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01810-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-01810-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01810-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T12:07:06Z","timestamp":1648037226000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-01810-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,23]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1810"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-01810-z","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,23]]},"assertion":[{"value":"10 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","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 research methods performed in this research are in accordance with the principles of medical ethics and ethical principles in \u201cDeclaration of Helsinki\u201d, \u201cInternational ethical guidelines for biomedical research involving human subjects\u201d promulgated by the Council for International Organizations of Medical Sciences (CIOMS), and relevant international ethical guidelines and regulations. The Ethical review committee of the Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College granted exempt status for this study and waived the need for informed consent because of no identifiable personal information or data in the clinical corpus used in this project. The project is certified with no ethical issues involved.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"72"}}