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Article(id=1322630906880311466, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322630794611380728, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.18.024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1761667200000, receivedDateStr=2025-10-29, revisedDate=1781193600000, revisedDateStr=2026-06-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1793269381494, onlineDateStr=2026-10-29, pubDate=1790265600000, pubDateStr=2026-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793269381494, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793269381494, creator=13701087609, updateTime=1793269381494, updator=13701087609, issue=Issue{id=1322630794611380728, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='18', pageStart='3153', pageEnd='3344', issueExtLink='null', onlineDate='null', pubDate='1790265600000', pubDateStr='2026-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793269354727, creator='13701087609', updateTime=1793343080645, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940023645011984, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322630794611380728, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940023645011985, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322630794611380728, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3306, endPage=3312, ext={EN=ArticleExt(id=1322630907102609579, articleId=1322630906880311466, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application of natural language processing in patients with rheumatoid arthritis:a scoping review, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To summarize studies on the application of natural language processing in patients with rheumatoid arthritis.

Methods

Guided by the scoping review methodological framework,relevant databases were searched from inception to April 18,2025.The included literature was synthesized and analyzed.

Results

A total of 16 articles were included.The application scenarios of natural language processing in the field of rheumatoid arthritis included focusing on patients with rheumatoid arthritis-associated interstitial lung disease,constructing knowledge graphs,exploring rheumatoid arthritis medications,and supporting machine learning.The application methods were diverse.The application effects were excellent.

Conclusions

Natural language processing has broad application prospects in patients with rheumatoid arthritis.However,relevant studies are currently limited.Future research should strengthen model interpretability,address the challenges of multimodal text integration,and further expand application scope,so as to evaluate its effectiveness and reliability comprehensively.

, authors=Yifan GUO, Sanjiao LIU, Hua REN, Jing BAI, authorsList=Yifan GUO, Sanjiao LIU, Hua REN, Jing BAI, authorCompany=null, correspAuthors=Hua REN, 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=1322630907945664686, articleId=1322630906880311466, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=自然语言处理在类风湿关节炎病人中应用的范围综述, columnId=1220689485482873504, journalTitle=护理研究, columnName=科研综述, runingTitle=null, highlight=null, articleAbstract=
目的

对自然语言处理在类风湿关节炎病人中应用的相关研究进行综述。

方法

以范围综述方法学框架为指导，检索国内外数据库，检索时限为建库至2025年4月18日，对纳入文献进行整合分析。

结果

共纳入16篇文献。自然语言处理在类风湿关节炎领域的应用场景包括聚焦类风湿关节炎相关间质性肺病病人、构建知识图谱、探讨类风湿关节炎药物、辅助机器学习等，其应用方法丰富多元，应用效果表现优异。

结论

自然语言处理在类风湿关节炎病人中具有广泛的应用前景，但目前相关研究较少，未来应加强模型的可解释性，破解多模态文本融合难题，进一步拓展应用范围并进行验证，以全面评估其有效性和可靠性。

, authors=郭伊凡, 刘三娇, 任桦*, *, 白婧, authorsList=郭伊凡, 刘三娇, 任桦, 白婧, authorCompany=null, correspAuthors=任桦, authorNote=

郭伊凡，护士，硕士研究生在读

, correspAuthorsNote=
任桦，E-mail:
REN Hua, E-mail:
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unstructuredReference=Madaudo C,Parlati A,Di Lisi D,et al.Artificial intelligence in cardiology:a peek at the future and the role of ChatGPT in cardiology practice[J].J Cardiovasc Med(Hagerstown),2024,25(11):766-771., articleTitle=Artificial intelligence in cardiology:a peek at the future and the role of ChatGPT in cardiology practice, refAbstract=null), Reference(id=1322630919186399483, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, doi=null, pmid=null, pmcid=null, year=2025, volume=110, issue=3, pageStart=e841, pageEnd=e850, url=null, language=null, rfNumber=41, rfOrder=40, authorNames=Guo S, Li R, Li G, journalName=J Clin Endocrinol Metab, refType=null, unstructuredReference=Guo S,Li R,Li G,et al.Comparing ChatGPT's and surgeon's responses to thyroid-related questions from patients[J].J Clin Endocrinol Metab,2025,110(3):e841-e850., articleTitle=Comparing ChatGPT's and surgeon's responses to thyroid-related questions from patients, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322630908285403311, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, xref=1, ext=[AuthorCompanyExt(id=1322630908293791920, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, companyId=1322630908285403311, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1School of Nursing, Shanxi University of Chinese Medicine, Shanxi 030619 China), AuthorCompanyExt(id=1322630908306374833, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, companyId=1322630908285403311, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1山西中医药大学护理学院，山西 030619)])], figs=[ArticleFig(id=1322630911649235151, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, language=EN, label=Figure 1, caption=Literature screening process and results, figureFileSmall=ejgsuXgc6LdpQTG2rg9WOQ==, figureFileBig=sklIca+ylkF0LLXR3F9AhA==, tableContent=null), ArticleFig(id=1322630911712149712, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, language=CN, label=图1, caption=文献筛选流程及结果, figureFileSmall=ejgsuXgc6LdpQTG2rg9WOQ==, figureFileBig=sklIca+ylkF0LLXR3F9AhA==, tableContent=null), ArticleFig(id=1322630911917670609, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, language=EN, label=Table 1, caption=

Basic characteristics of included literature studies

, figureFileSmall=null, figureFileBig=null, tableContent=
纳入研究国家数据来源疾病应用方法应用效果
Wang等[20]2018美国电子病历、美国食品药品监督管理局不良事件报告系统RA使用NLP技术从非结构化文本中提取药物和临床结果信息，并将其标准化为统一医学语言系统代码结合美国食品药品监督管理局不良事件报告系统和电子病历数据可显著提高药物不良事件检测的召回率和精确度
Gronsbell等[21]2019美国电子健康记录RA使用NLP技术从临床笔记中提取与RA相关的临床概念并进行文本分类，将其作为特征用于模型训练NLP提取的特征使RA表型识别模型在100个金标准标签时受试者工作特征曲线下面积达0.927，200个金标准标签时受试者工作特征曲线下面积达0.945，且只需传统方法一半标签便可实现同等精度
Kronzer等[22]2020美国电子健康记录RA使用NLP技术从电子健康记录数据中提取信息，评估模型在不同RA病人病程和电子健康长期记录中的性能eMERGE算法对早期RA病人识别能力较弱，但随着RA病程和电子健康记录持续时间延长，模型性能提高
Maarseveen等[23]2020荷兰、德国电子健康记录RA使用NLP技术对自由文本字段进行预处理和特征提取，并结合机器学习算法精准识别RA病人NLP技术在荷兰数据集识别出2 873例RA病人，阳性预测值为0.94，敏感性为0.93；在德国阳性预测值为0.97，敏感性为0.84
Sharma等[24]2020澳大利亚社交媒体平台RA利用Treato平台分析病人对疾病修饰抗风湿药物的情感倾向对54 742篇帖子分析发现，公众对疾病修饰抗风湿药物持积极态度，且负面情绪帖子中常见主题是缺乏疗效和副作用
孙明俊等[25]2021中国电子病历RA使用NLP技术从病历文本中提取关键信息，进行文本的实体识别、同义词归一化、证型判断、药方推荐及知识图谱构建系统可智能判断RA及证型，推荐药方，并通过知识图谱辅助医生诊疗
Cai等[26]2021美国电子健康记录RA使用NLP技术从临床笔记中提取医学概念，并结合结构化数据训练机器学习模型，提升临床试验病人筛选效率病历审查量减少40.5%~57.0%，灵敏度为98.3%~100.0%
Li等[27]2022中国电子病历RA使用NLP技术生成词向量，并将其输入模型中，提高临床笔记中医学实体识别的准确性和效率模型在测试集的F1分数为0.936，优于传统模型
张顺[28]2023中国电子病历RA使用NLP技术从医疗文本中提取实体和关系，并构建RA知识图谱构建了包含735个实体和3 718个关系的RA知识图谱，且实体识别和关系抽取准确率为87.95%和93.53%
Liu等[29]2023中国电子病历RA通过自动化的知识抽取方法从非结构化的电子病历中提取实体和关系，并构建知识图谱模型在实体识别和关系提取任务中F1分数分别为92.96%和95.29%，并构建包含6 465个实体和15 268个关系的RA知识图谱
Humbert-Droz等[30]2023美国美国各地风湿病学门诊病人的电子健康记录数据RA开发NLP管道，用从非结构化的风湿病学门诊笔记中提取RA的疾病活动和功能状态评估分数NLP管道内部验证敏感性为95%，阳性预测值为87%，F1分数为91%，与结构化数据一致性良好（0.43~0.98）；外部验证敏感性为92%，阳性预测值为69%，F1分数为79%，泛化能力强
Luedders等[31]2023美国电子健康记录、美国退伍军人事务部RA、RA相关间质性肺病通过自动正则表达式从胸部CT报告中提取间质性肺病相关术语算法的阳性预测值在推导队列中提升3.6%~11.7%，在验证队列中提升6.0%~21.1%
Li等[32]2023中国自建库RA利用NLP中的语义文本相似度计算技术，开发针对RA的智能问答系统改进模型在测试集Top-1、Top-3、Top-5的精确度分别为0.551，0.767，0.813
Coskun等[33]2024土耳其先前建立的23个问题RA比较4种大语言模型在回答氨甲蝶呤相关问题时的准确性和完整性作用机制方面，ChatGPT和BARD优于Bing；潜在副作用方面，ChatGPT4优于BARD；生活方式方面，各模型间差异不明显
England等[34]2024美国美国退伍军人事务部-电子健康记录RA相关间质性肺病开发NLP程序，从临床笔记中提取用力肺活量，并与肺功能测试设备记录值对比验证与单独使用肺功能测试设备相比，NLP程序将捕获用力肺活量的数量提高近3倍
Román等[35]2024西班牙9所医院的电子健康记录RA、RA相关间质性肺病从电子健康记录中提取非结构化临床信息，标准化为系统化医学命名法-临床术语，以估计RA和RA相关间质性肺病的患病率,并比较两组病人的临床特征RA和RA相关间质性肺病的年龄校正后患病率分别为0.53%和0.02%；RA相关间质性肺病相较于无间质性肺病的病人年龄较大、合并症较多、炎症负担较重、院内死亡率较高
), ArticleFig(id=1322630911997362386, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322630906880311466, language=CN, label=表1, caption=

纳入文献研究的基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
纳入研究国家数据来源疾病应用方法应用效果
Wang等[20]2018美国电子病历、美国食品药品监督管理局不良事件报告系统RA使用NLP技术从非结构化文本中提取药物和临床结果信息，并将其标准化为统一医学语言系统代码结合美国食品药品监督管理局不良事件报告系统和电子病历数据可显著提高药物不良事件检测的召回率和精确度
Gronsbell等[21]2019美国电子健康记录RA使用NLP技术从临床笔记中提取与RA相关的临床概念并进行文本分类，将其作为特征用于模型训练NLP提取的特征使RA表型识别模型在100个金标准标签时受试者工作特征曲线下面积达0.927，200个金标准标签时受试者工作特征曲线下面积达0.945，且只需传统方法一半标签便可实现同等精度
Kronzer等[22]2020美国电子健康记录RA使用NLP技术从电子健康记录数据中提取信息，评估模型在不同RA病人病程和电子健康长期记录中的性能eMERGE算法对早期RA病人识别能力较弱，但随着RA病程和电子健康记录持续时间延长，模型性能提高
Maarseveen等[23]2020荷兰、德国电子健康记录RA使用NLP技术对自由文本字段进行预处理和特征提取，并结合机器学习算法精准识别RA病人NLP技术在荷兰数据集识别出2 873例RA病人，阳性预测值为0.94，敏感性为0.93；在德国阳性预测值为0.97，敏感性为0.84
Sharma等[24]2020澳大利亚社交媒体平台RA利用Treato平台分析病人对疾病修饰抗风湿药物的情感倾向对54 742篇帖子分析发现，公众对疾病修饰抗风湿药物持积极态度，且负面情绪帖子中常见主题是缺乏疗效和副作用
孙明俊等[25]2021中国电子病历RA使用NLP技术从病历文本中提取关键信息，进行文本的实体识别、同义词归一化、证型判断、药方推荐及知识图谱构建系统可智能判断RA及证型，推荐药方，并通过知识图谱辅助医生诊疗
Cai等[26]2021美国电子健康记录RA使用NLP技术从临床笔记中提取医学概念，并结合结构化数据训练机器学习模型，提升临床试验病人筛选效率病历审查量减少40.5%~57.0%，灵敏度为98.3%~100.0%
Li等[27]2022中国电子病历RA使用NLP技术生成词向量，并将其输入模型中，提高临床笔记中医学实体识别的准确性和效率模型在测试集的F1分数为0.936，优于传统模型
张顺[28]2023中国电子病历RA使用NLP技术从医疗文本中提取实体和关系，并构建RA知识图谱构建了包含735个实体和3 718个关系的RA知识图谱，且实体识别和关系抽取准确率为87.95%和93.53%
Liu等[29]2023中国电子病历RA通过自动化的知识抽取方法从非结构化的电子病历中提取实体和关系，并构建知识图谱模型在实体识别和关系提取任务中F1分数分别为92.96%和95.29%，并构建包含6 465个实体和15 268个关系的RA知识图谱
Humbert-Droz等[30]2023美国美国各地风湿病学门诊病人的电子健康记录数据RA开发NLP管道，用从非结构化的风湿病学门诊笔记中提取RA的疾病活动和功能状态评估分数NLP管道内部验证敏感性为95%，阳性预测值为87%，F1分数为91%，与结构化数据一致性良好（0.43~0.98）；外部验证敏感性为92%，阳性预测值为69%，F1分数为79%，泛化能力强
Luedders等[31]2023美国电子健康记录、美国退伍军人事务部RA、RA相关间质性肺病通过自动正则表达式从胸部CT报告中提取间质性肺病相关术语算法的阳性预测值在推导队列中提升3.6%~11.7%，在验证队列中提升6.0%~21.1%
Li等[32]2023中国自建库RA利用NLP中的语义文本相似度计算技术，开发针对RA的智能问答系统改进模型在测试集Top-1、Top-3、Top-5的精确度分别为0.551，0.767，0.813
Coskun等[33]2024土耳其先前建立的23个问题RA比较4种大语言模型在回答氨甲蝶呤相关问题时的准确性和完整性作用机制方面，ChatGPT和BARD优于Bing；潜在副作用方面，ChatGPT4优于BARD；生活方式方面，各模型间差异不明显
England等[34]2024美国美国退伍军人事务部-电子健康记录RA相关间质性肺病开发NLP程序，从临床笔记中提取用力肺活量，并与肺功能测试设备记录值对比验证与单独使用肺功能测试设备相比，NLP程序将捕获用力肺活量的数量提高近3倍
Román等[35]2024西班牙9所医院的电子健康记录RA、RA相关间质性肺病从电子健康记录中提取非结构化临床信息，标准化为系统化医学命名法-临床术语，以估计RA和RA相关间质性肺病的患病率,并比较两组病人的临床特征RA和RA相关间质性肺病的年龄校正后患病率分别为0.53%和0.02%；RA相关间质性肺病相较于无间质性肺病的病人年龄较大、合并症较多、炎症负担较重、院内死亡率较高
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自然语言处理在类风湿关节炎病人中应用的范围综述
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郭伊凡 , 刘三娇 , 任桦 *, * , 白婧
护理研究 | 科研综述 2026,40(18): 3306-3312
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护理研究 |科研综述 2026 , 40 (18) : 3306 -3312
自然语言处理在类风湿关节炎病人中应用的范围综述
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郭伊凡, 刘三娇, 任桦*, * , 白婧
作者信息
  • 1山西中医药大学护理学院，山西 030619
通讯作者:
任桦，E-mail:
REN Hua, E-mail:
作者简介:

郭伊凡，护士，硕士研究生在读

Application of natural language processing in patients with rheumatoid arthritis:a scoping review
Yifan GUO, Sanjiao LIU, Hua REN , Jing BAI
Affiliations
  • 1School of Nursing, Shanxi University of Chinese Medicine, Shanxi 030619 China
出版时间: 2026-09-25 doi: 10.12102/j.issn.1009-6493.2026.18.024
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摘要
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目的

对自然语言处理在类风湿关节炎病人中应用的相关研究进行综述。

方法

以范围综述方法学框架为指导，检索国内外数据库，检索时限为建库至2025年4月18日，对纳入文献进行整合分析。

结果

共纳入16篇文献。自然语言处理在类风湿关节炎领域的应用场景包括聚焦类风湿关节炎相关间质性肺病病人、构建知识图谱、探讨类风湿关节炎药物、辅助机器学习等，其应用方法丰富多元，应用效果表现优异。

结论

自然语言处理在类风湿关节炎病人中具有广泛的应用前景，但目前相关研究较少，未来应加强模型的可解释性，破解多模态文本融合难题，进一步拓展应用范围并进行验证，以全面评估其有效性和可靠性。

自然语言处理  /  类风湿关节炎  /  人工智能  /  范围综述
Objective

To summarize studies on the application of natural language processing in patients with rheumatoid arthritis.

Methods

Guided by the scoping review methodological framework,relevant databases were searched from inception to April 18,2025.The included literature was synthesized and analyzed.

Results

A total of 16 articles were included.The application scenarios of natural language processing in the field of rheumatoid arthritis included focusing on patients with rheumatoid arthritis-associated interstitial lung disease,constructing knowledge graphs,exploring rheumatoid arthritis medications,and supporting machine learning.The application methods were diverse.The application effects were excellent.

Conclusions

Natural language processing has broad application prospects in patients with rheumatoid arthritis.However,relevant studies are currently limited.Future research should strengthen model interpretability,address the challenges of multimodal text integration,and further expand application scope,so as to evaluate its effectiveness and reliability comprehensively.

natural language processing  /  rheumatoid arthritis  /  artificial intelligence  /  scoping review
郭伊凡, 刘三娇, 任桦, 白婧. 自然语言处理在类风湿关节炎病人中应用的范围综述. 护理研究, 2026 , 40 (18) : 3306 -3312 . DOI: 10.12102/j.issn.1009-6493.2026.18.024
Yifan GUO, Sanjiao LIU, Hua REN, Jing BAI. Application of natural language processing in patients with rheumatoid arthritis:a scoping review[J]. Chinese Nursing Research, 2026 , 40 (18) : 3306 -3312 . DOI: 10.12102/j.issn.1009-6493.2026.18.024
正文
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类风湿关节炎（rheumatoid arthritis，RA）是一种以关节及关节周围软组织受累为主要特征的慢性、全身性自身免疫性疾病，具有高致残风险[1]，可影响多个器官，给病人及其家庭带来沉重负担[2]。已有数据显示，RA全球患病率为0.25%~1.00%[3]。我国患病率约为0.42%，病人约500万例[4]。RA发病机制复杂，目前尚未完全明确，无法做到全面根治[1]。在临床实践中，医生会整合病人相关信息并记录到临床笔记中，便于日后查阅与研究，以优化疾病诊疗效果[5]。但临床笔记中的信息多为非结构化文本[6]，缺乏标准化格式，导致其在原始状态下难以进行大规模分析。自然语言处理（natural language processing，NLP）能从非结构化的临床笔记中提取信息，并进行分类、整合和分析等[7]，将其应用于RA研究领域，有利于精准识别RA病人，但就目前而言，尚未检索到针对NLP技术在RA研究领域进行分类和应用效果总结的研究。范围综述是基于循证实践理念的证据总结方法，可快速识别某领域的研究进展，总结研究成果、发现现存问题[8]。本研究拟采用Arksey等[9]提出的范围综述方法学框架，对国内外NLP在RA病人中的应用研究进行全面梳理，以明确当前研究的不足和未来发展方向，为医护人员开展相关研究提供参考。
1　资料与方法
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1.1　NLP介绍
NLP是人工智能领域的关键研究分支，综合了语言学、计算机科学、数学及心理学等多个学科的知识，目的是借助计算机技术实现语言的分析处理[10]，并赋予计算机与人类相似的语言沟通和文本解析能力[11]。NLP能够分析大规模的书面和口头语言数据，包括社交媒体帖子、电子健康记录和临床笔记等[12]。典型的NLP任务包括信息提取[13]、主题建模[14]、情感分析[15]和文本分类[16]等。
在发展过程中，NLP逐渐形成了一些主要的研究范式，包括基于规则的方法、基于机器学习的方法以及基于深度学习的方法[17]，其中，基于规则的方法是通过预先定义的规则和模式解析和理解语言，这些规则通常由语言学家和领域专家根据语言结构和语义制定，适用于结构化和相对简单的语言任务。基于机器学习的方法是通过算法从大量数据中自动学习语言模式，无需人工制定规则，适用于处理非结构化和复杂的语言环境。基于深度学习的方法是利用多层神经网络从海量数据中自动提取和学习语言的深层次特征，与基于机器学习的方法相比，能更好地处理语言的复杂性和抽象性问题，如大语言模型（large language models，LLMs）。
1.2　确定研究问题
查阅文献确定研究问题：1）NLP在RA病人中的应用场景如何？2）NLP在RA病人中具体的应用方法有哪些？3）NLP在RA病人中应用的效果怎样？
1.3　检索策略
以“类风湿关节炎”“类风湿性关节炎”“自然语言处理”“大语言模型”“生成式预训练变换器”“GPT”“ChatGPT”“BERT”“RoBERTa”“Gemini”“PaLM”“LLaMA”“ALBERT”“ELECTRA”等为中文检索词，以“arthritis,rheumatoid”“rheumatoid arthritis”“natural language processing”“NLP”“language model*”“large language model*”“LLM”“LLMs”“transformer language model*”“generative pretrained transformer”等为英文检索词，检索中国知网、万方数据知识服务平台、维普网、中国生物医学文献数据库、PubMed、EMbase、Web of Science和Cochrane Library，检索时限为建库至2025年4月18日。英文数据库以PubMed为例，检索策略如下：
#1 （arthritis,rheumatoid[MeSH]） OR （rheumatoid arthritis[Title/Abstract])
#2 （（（（（（（（（（（（（（（（（natural language processing[MeSH Terms]） OR （natural language processing[Title/Abstract])） OR （NLP[Title/Abstract])）OR （language model*[Title/Abstract])）OR （large language model*[Title/Abstract])） OR （LLM[Title/Abstract])） OR （LLMs[Title/Abstract])） OR （transformer language model*[Title/Abstract])）OR （generative pretrained transformer[Title/Abstract])） OR （GPT[Title/Abstract])）OR （ChatGPT[Title/Abstract])）OR （BERT[Title/Abstract])）OR （RoBERTa[Title/Abstract])）OR （Gemini[Title/Abstract])）OR （LLaMA[Title/Abstract])）OR （PaLM[Title/Abstract])）OR （ALBERT[Title/Abstract])）OR （ELECTRA[Title/Abstract])
#3 #1 AND #2
1.4　文献纳入与排除标准
纳入标准：1）研究对象符合美国风湿病学会（ACR）1987年修订的RA分类标准[18]或2010年ACR和欧洲抗风湿病联盟（EULAR）提出的RA分类标准[19]；2）研究内容涉及NLP在RA病人中的应用；3）文献类型为原始研究。排除标准：1）综述类研究；2）无法获取全文或仅有会议摘要的文献；3）非中英文文献。
1.5　文献筛选与资料提取
将检索获得的文献导入NoteExpress软件去重，随后进行人工筛查，由2名经过培训的研究者独立阅读文献标题和摘要进行初筛，再阅读全文进行复筛，确定最终纳入的文献。筛选过程中若出现分歧，则由第3名研究人员参与讨论后决定是否纳入。资料提取内容包括第一作者、发表时间、国家、数据来源、疾病、应用方法、应用效果。
2　结果
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2.1　文献筛选结果
检索数据库获得相关文献1 005篇，其他资源补充文献1篇，经去重、阅读题目、摘要及全文后，最终纳入文献16篇[20-35]。文献筛选流程及结果见图1。
2.2　纳入文献研究的基本特征
16篇文献[20-35]发表时间为2018—2024年，其中，英文文献14篇[20-24,26-27,29-35]，中文文献2篇[25,28]。纳入文献的基本特征见表1。
2.3　NLP在RA病人中的应用场景
2.3.1　聚焦RA相关间质性肺病病人
3项研究[31,34-35]将NLP技术应用于RA相关间质性肺病病人。Luedders等[31]通过从胸部CT报告中提取与RA相关间质性肺病有关的术语，利用NLP技术改进现有的行政数据算法，经外部验证后算法的特异性、准确性、阳性预测值均有所提高，识别RA相关间质性肺病的准确性提高。Román等[35]使用机器学习和NLP技术从电子健康记录中提取非结构化数据，发现RA相关间质性肺病的流行率为5.1%，且RA相关间质性肺病病人与非间质性肺病病人相比更年长、共病率更高、炎症负担更重，院内死亡风险显著增加。England等[34]开发了一种可以从电子健康记录中提取RA相关间质性肺病病人用力肺活量数据的NLP工具，结果表明，该工具能将用力肺活量的捕获量提高近3倍，并且与直接从肺功能测试设备获得的数值高度一致。
2.3.2　构建知识图谱
3项研究[25,28-29]使用NLP技术构建RA病人知识图谱。孙明俊等[25]基于中医典籍，应用NLP技术对病历文本进行实体识别和关系抽取，构建RA中医辅助诊疗系统的知识图谱，该系统能够依据中医诊疗原则智能推荐药方，从而制定基于证据的治疗方案，有效辅助医生进行诊疗。张顺[28]基于电子病历，使用半监督学习框架抽取医疗实体和关系，构建并可视化一个包含735个实体和3 718个关系的RA知识图谱，为医疗专业人员提供了一个直观、整合且动态更新的决策支持工具。Liu等[29]基于电子病历，通过半监督学习框架，利用预训练的医学语言模型与深度神经网络从少量手动标注的中文电子病历样本和大量未标注数据中实现高效的自动知识提取，最终构建了一个包含6 465个实体和15 268个关系的RA知识图谱。
2.3.3　探讨RA药物
3项研究[20,24,33]通过NLP技术分别探讨了RA药物不良事件监测、病人感知以及药物信息人工智能准确性。Wang等[20]开发了一种疾病修饰抗风湿药物在RA病人中潜在药物不良事件的检测方法，以提高药物警戒的效率和准确性，结果表明,结合美国食品药品监督管理局不良事件报告系统和电子病历数据可显著提高药物不良事件检测的召回率，尤其是采用灵活的映射策略时能够更全面地识别药物不良事件。Sharma等[24]分析社交媒体RA病人对疾病修饰抗风湿药物的讨论发现，疗效是病人正面情感的主要原因，缺乏疗效和副作用是病人负面情感的主要原因，其中氨甲蝶呤因副作用使病人产生了较多负面情感，而生物药物则因疗效显著获得了病人较多的正面评价。Coskun等[33]评估了4种大语言模型（ChatGPT3.5、ChatGPT4、BARD、Bing）在回答氨甲蝶呤治疗RA相关问题的准确性和完整性时发现，ChatGPT模型表现优于BARD和Bing，但所有模型均存在一定的不准确性。
2.3.4　辅助机器学习
5项研究[21-23,26-27]使用NLP技术结合机器学习方法构建预测模型或分类算法。Gronsbell等[21]利用NLP技术处理非结构化数据，并使用无监督学习方法和正则化回归选择与疾病表型相关的特征，结果开发了一种自动化特征选择方法，减少了对人工标注数据的依赖。在实际的电子健康记录数据中，该方法能够有效识别RA病人，且比传统方法需要的金标准标签更少。Kronzer等[22]使用NLP工具获取每个概念在临床笔记中被积极提及的次数，并结合逻辑回归方法构建RA分类算法，结果表明，算法性能随着RA病程迁延和健康记录持续时间延长而提高，但对早期识别RA的能力较弱。Maarseveen等[23]使用NLP技术对自由文本字段进行预处理（包括词分割、小写转换、停用词去除、词形归一化和向量化），然后比较了6种机器学习方法（支持向量机、梯度提升等）性能，选择最佳方法构建分类器，结果显示，该方法能够从电子健康记录数据中精准提取RA病人的记录，且在不同中心的数据上具有良好的可移植性。Cai等[26]利用NLP技术从临床笔记中提取与RA临床试验招募相关的概念，使用集成学习方法构建资格筛查算法，最后生成SMALL模型，该模型能够在不降低筛选质量的前提下减少手动审查工作量，从而加快临床试验病人的筛选过程，节省时间和资源。Li等[27]采用BERT预训练模型增强词向量的语义表示，并将其与传统的双向长短期记忆网络（BiLSTM）和条件随机场（CRF）机器学习算法相结合，构建了用于临床笔记命名实体识别的模型，结果表明，与传统词向量模型相比，该模型表现优异，最佳F1分数可达0.936。
2.3.5　其他应用
Humbert-Droz等[30]开发了一个NLP管道，用于从多个电子健康记录系统中提取RA的疾病活动度和功能状态评估分数，该NLP管道包括3个模块，即预处理原始文本；使用主题专家策划的术语进行模块识别；清理和验证提取的RA结果指标和相关分数，生成全局注释表。结果发现，该管道具有良好的内部和外部有效性，可用于临床研究和质量测量。Li等[32]使用NLP技术改进了基于常见问题的智能问答系统，用于RA领域的知识查询，该系统能够更好地理解用户问题的深层含义，并提供与原始查询更相关的答案。
3　讨论
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3.1　NLP在RA领域的应用尚未得到充分探索
本研究纳入的16项研究在发表时间、地区分布、NLP技术应用方面分布不均衡。发表时间以2020年及以后为主（14项研究，占87.5%），发表数量最多的国家是美国，共7项研究[20-22,26,30-31,34]，其次是我国，共5项研究[25,27-29,32]，土耳其[33]、西班牙[35]、澳大利亚[24]、荷兰和德国联合[23]各发表1项研究。除Coskun等[33]的研究外，NLP技术应用均涉及信息提取；6项研究[23-24,26-27,32-33]涉及文本分类任务；Sharma等[24]研究还包含情感分析和主题建模。部分研究对成果进行了验证，但未直接应用于临床实践。可见，NLP在RA领域的应用仍处于探索阶段，缺乏公开共享的数据集可能是导致NLP研究有限的原因之一。建议未来在RA领域加强数据集的公开与共享，以促进相关研究的深入开展；同时，进一步加强临床试验和多中心研究，以验证其在不同病人群体中的有效性；此外，可开展质性研究，深入了解临床医护人员的态度，加强沟通与合作，共同推动NLP技术在RA领域的临床应用。
3.2　NLP在RA病人数据处理方面具有较大优势
在数字信息技术与大数据蓬勃发展的背景下，NLP技术发展迅速，并在RA病人相关诊断、治疗和管理中彰显出独特优势。NLP能够挖掘电子健康记录中隐藏的海量自由文本信息，将描述性文本转化为有价值的数据和见解[36]。且引入机器学习方法后，NLP模型的训练变得更加高效，使其在RA领域的应用更加广泛和精准，如可预测疾病表型[21]、精准识别RA病人[22-23]、提升临床试验病人筛选效率[26]以及精准识别和分类医学实体[27]。其次，NLP可减轻临床医生手动审查非结构化临床笔记的负担，提高临床决策质量和效率。手动审查耗时、成本较高且易遗漏关键信息，而NLP能实时分析数据，精准提取并向临床医生呈现重要信息[37-38]。再次，NLP技术还可应用于RA相关间质性肺病病人，为未来疾病诊断、严重程度评估和流行病学研究提供新的方法和思路。此外，整合NLP、大语言模型和生成式AI技术的ChatGPT[39]能够精准解析复杂的语言模式，并生成流畅自然的类人文本，通过模拟与用户的互动交流，针对各类主题输出贴合语境的回答，在医学领域得到广泛应用[40-41]。Coskun等[33]聚焦于包含ChatGPT在内的4种大语言模型，对其在回答氨甲蝶呤相关问题时的准确性和完整性进行比较，未来或可助力医务人员更高效地向病人普及氨甲蝶呤用药知识，提升病人对药物的认知与管理能力。
3.3　对未来研究的启示
NLP在RA领域的应用较少，且大多研究处于试验、开发和验证阶段，较少应用于临床实践。未来可围绕以下3个方面展开深度探究。1）增强模型的可解释性：纳入研究中关于NLP技术结合机器学习方法构建的模型涉及可解释性的相关内容较少，未来应关注模型可解释性的提升，通过引入特征重要性分析、可视化技术等方法，进一步探究如何在保证模型性能基础上使其决策过程更加透明且易于理解，从而更好地将模型应用于临床实践。2）破解多模态文本融合难题：文本是多模态数据的核心，现有研究中NLP与影像的融合多停留在浅层特征匹配，与结构化数据的整合依赖人工规则，未来研究需通过跨模态语义嵌入技术构建文本与影像的语义关联，利用实体链接实现文本与结构化数据的互补融合，进而提高多模态数据的应用价值。3）拓展应用与验证：研究结果需进一步探讨NLP在多种临床文本和环境中的应用，并在真实世界临床环境中开展更多前瞻性验证，以全面评估其有效性和可靠性。
3.4　小结
本研究分析了NLP技术在RA领域中的应用情况，梳理了NLP在RA病人中的应用场景等。总体来看，NLP在RA疾病表型的识别和分类以及利用非结构化数据提升预测性能方面表现良好，但在模型可解释性、多模态文本融合及模型验证方面仍存在不足，未来应确保临床医生深度参与算法开发，以进一步拓展NLP在RA领域的应用。

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doi: 10.12102/j.issn.1009-6493.2026.18.024
  • 接收时间：2025-10-29
  • 首发时间：2026-10-29
  • 出版时间：2026-09-25
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  • 收稿日期：2025-10-29
  • 修回日期：2026-06-12
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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