{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:56Z","timestamp":1782846236716,"version":"3.54.5"},"reference-count":34,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The unstructured data of Chinese cancer electronic health records (EHRs) contains valuable medical expertise. Accurate medical entity recognition is crucial for building a medical-assisted decision system. Named entity recognition (NER) in cancer EHRs typically uses general models designed for English medical records. There is a lack of specialized handling for cancer-specific records and limited application to Chinese medical records.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to propose a specific NER model to enhance the recognition of medical entities in Chinese cancer EHRs.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Desensitized inpatient EHRs related to breast cancer were collected from a leading hospital in Beijing. Building upon the MC Bidirectional Encoder Representations from Transformers (BERT) foundation, the study further incorporated a Chinese cancer corpus for pretraining, resulting in the construction of the ChCancerBERT pretrained model. In conjunction with dilated-gated convolutional neural networks, bidirectional long short-term memory, multihead attention mechanism, and a conditional random field, this model forms a multimodel, multilevel integrated NER approach.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n This approach effectively extracts medical entity features related to symptoms, signs, tests, treatments, and time in Chinese breast cancer EHRs. The entity recognition performance of the proposed model surpasses that of the baseline model and other models compared in the experiment. The\n F<\/jats:italic>\n 1<\/jats:sub>\n -score reached 86.93%, precision reached 87.24%, and recall reached 86.61%. The model introduced in this study demonstrates exceptional performance on the CCKS2019 dataset, attaining a precision rate of 87.26%, a recall rate of 87.27%, and an impressive\n F<\/jats:italic>\n 1<\/jats:sub>\n -score of 87.26%, surpassing that of existing models.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The experiments demonstrate that the approach proposed in this study exhibits excellent performance in NER within breast cancer EHRs. This advancement will further contribute to clinical decision support for cancer treatment and research. In addition, the study reveals that incorporating domain-specific corpora in clinical NER tasks can further enhance the performance of BERT models in specialized domains.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/76912","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T20:42:30Z","timestamp":1763152950000},"page":"e76912-e76912","source":"Crossref","is-referenced-by-count":0,"title":["Named Entity Recognition for Chinese Cancer Electronic Health Records\u2014Development and Evaluation of a Domain-Specific BERT Model: Quantitative Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3536-7811","authenticated-orcid":false,"given":"Junbai","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9278-6685","authenticated-orcid":false,"given":"Butian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9161-2701","authenticated-orcid":false,"given":"Xiaohan","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1303-2885","authenticated-orcid":false,"given":"Zhengkai","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1806-0688","authenticated-orcid":false,"given":"Ruojia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1617-6110","authenticated-orcid":false,"given":"Jiarui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8391-9412","authenticated-orcid":false,"given":"Songxing","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9982-3784","authenticated-orcid":false,"given":"Fengying","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105246","article-title":"Machine learning models to detect and predict patient safety events using electronic health records: a systematic review","volume":"180","author":"Deimazar","journal-title":"Int J Med Inform"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1177\/15347354231178898","article-title":"Chinese herbal medicine improved survival in stage IV breast cancer patients: data mining of the Incorporated Taiwan Cancer Registry Database and hospital database","volume":"22","author":"Chen","journal-title":"Integr Cancer Ther"},{"issue":"4","key":"R3","doi-asserted-by":"publisher","first-page":"1899","DOI":"10.1007\/s12325-023-02425-0","article-title":"Clinical characteristics, treatment patterns, and effectiveness in Chinese patients with angina pectoris using electronic patient-reported outcomes: protocol for a multicenter, prospective, cohort study (GREAT)","volume":"40","author":"Zhao","journal-title":"Adv Ther"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3240898","article-title":"Systematic literature review of information extraction from textual data: recent methods, applications, trends, and challenges","volume":"11","author":"Abdullah","journal-title":"IEEE Access"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103422","article-title":"Chinese clinical named entity recognition with variant neural structures based on BERT methods","volume":"107","author":"Li","journal-title":"J Biomed Inform"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-02059-2","article-title":"Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT","volume":"22","author":"Chen","journal-title":"BMC Med Inform Decis Mak"},{"issue":"7","key":"R7","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1093\/jamia\/ocac040","article-title":"CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records","volume":"29","author":"Zhou","journal-title":"J Am Med Inform Assoc"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2023.08.018","article-title":"A cross-institutional evaluation on breast cancer phenotyping NLP algorithms on electronic health records","volume":"22","author":"Zhou","journal-title":"Comput Struct Biotechnol J"},{"key":"R9","doi-asserted-by":"crossref","unstructured":"Najafabadipour M Tunas JM Rodriguez-Gonzalez A . 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