Application of natural language processing in patients with rheumatoid arthritis:a scoping review.
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 analyze...
| Publicado en: | Chinese Nursing Research Vol. 40; no. 18; pp. 3306 - 3313 |
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| Autores principales: | , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Chinese Nursing Research Editorial Office
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. 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。 |
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