Article(id=1317125510908465482, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1317125500041015883, articleNumber=null, orderNo=null, doi=10.13193/j.issn.1673-7717.2026.02.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791956792809, onlineDateStr=2026-10-14, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791956792809, onlineIssueDateStr=2026-10-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791956792809, creator=13701087609, updateTime=1791956792809, updator=13701087609, issue=Issue{id=1317125500041015883, tenantId=1146029695717560320, journalId=1255847919539208197, year='2026', volume='44', issue='2', pageStart='1', pageEnd='258', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791956790217, creator='13701087609', updateTime=1791958795145, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317133909368877755, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1317125500041015883, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317133909373072060, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1317125500041015883, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=17, endPage=23, ext={EN=ArticleExt(id=1317125512586187083, articleId=1317125510908465482, tenantId=1146029695717560320, journalId=1255847919539208197, language=EN, title=Research on Multi-dimensional Named Entity Recognition in Traditional Chinese Medicine Cases, columnId=1256183375241535778, journalTitle=Chinese Archives of Traditional Chinese Medicine, columnName=Digital Traditional Chinese Medicine, runingTitle=null, highlight=null, articleAbstract=
Objective To solve the problem of poor recognition performance of current traditional Chinese medicine case naming entity recognition models，and to improve the efficiency and accuracy of the structuring of traditional Chinese medicine cases.
Methods A BDM-CRF named entity recognition neural network model was constructed to integrate multi-dimensional information. The model integrated Bidirectional Long Short-Term Memory（BiLSTM），Gated-Dilated Convolutional Neural Networks（DGCNN）and Multi-Head Attention（MHA）models for multi-dimensional feature extraction. The feature extraction results were constrained by the Conditional Random Field（CRF）model. The effectiveness of the model was verified through comparative experiments，ablation experiments，pre-training model comparison experiments and neural network structure comparison experiments，validating and demonstrating the extraction performance of the model through case analysis.
Results The model proposed in this study achieved an average improvement of 2.30% in precision，4.73% in recall and 3.53% in F1 score compared to those of the control model. The case analysis results indicated that the model in this study can correctly identify most of the entity content in the text of traditional Chinese medicine medical cases.
Conclusion The proposed model for the named entity recognition in traditional Chinese medicine（TCM）medical records，which integrates multi-dimensional information，can achieve better recognition results，effectively improve the accuracy and efficiency of TCM medical record structuring，and promote the intelligent processing of TCM medical records.
, authors=Junbai CHEN
1, Fengying GUO
1, Butian ZHAO
1, Songxing DU
2, Tian TIAN
3, Zhanfeng YAN
4, Li WANG
1, authorsList=Junbai CHEN, Fengying GUO, Butian ZHAO, Songxing DU, Tian TIAN, Zhanfeng YAN, Li WANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1317125513269858636, articleId=1317125510908465482, tenantId=1146029695717560320, journalId=1255847919539208197, language=CN, title=融合多维度信息的中医医案命名实体识别算法研究, columnId=1256183387140776248, journalTitle=中华中医药学刊, columnName=数字中医中药, runingTitle=null, highlight=null, articleAbstract=
目的 解决当前中医医案命名实体识别模型识别效果欠佳的问题，提升中医医案结构化的效率与精准度。
方法 构建融合多维度信息的BDM-CRF命名实体识别神经网络模型，融合双向长短期记忆网络模型（Bidirectional long short-term memory，BiLSTM）、门控空洞卷积神经网络模型（Gated-dilated convolutional neural networks，DGCNN）和多头注意力机制模型（Multi-head attention，MHA）进行多维度特征抽取，通过条件随机场（Conditional random field，CRF）模型对特征抽取结果进行约束输出；通过对比实验、消融实验、预训练模型对比实验及神经网络结构对比实验验证该模型的有效性，通过个案分析验证并展示模型的提取效果。
结果 本研究提出的模型比对照模型精确率平均提升2.30%、召回率平均提升4.73%、F1值平均提升3.53%。个案分析结果表明，本研究模型能正确识别出中医医案文本中的大部分实体内容。
结论 本研究提出的融合多维度信息的中医医案命名实体识别模型能取得更佳的识别效果，可有效提升中医医案结构化的精准度及效率，促进中医医案处理智能化。
, authors=陈隽柏
1, 郭凤英
1, 赵步天
1, 杜松星
2, 田甜
3, 闫占峰
4, 王丽
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