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在风湿病学专业期刊上绘制二十年的研究:用BERTopic进行主题建模分析

Alfredo Madrid-García¹, Dalifer Freites-Núñez², Beatriz Merino-Barbancho³

¹Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos, Prof. Martin Lagos s/n, Madrid 28040, Spain.

Therapeutic advances in musculoskeletal disease

|December 30, 2024

概括

这项研究使用了主题建模来分析23年的风湿病学研究,揭示了从抗脂综合征等疾病到JAK抑制剂和COVID-19等新兴领域的焦点转移.

科学领域:

风湿病学研究的研究.
自然语言处理 (NLP) 是一种自然语言处理.
图书统计学图书统计学

背景情况:

风湿病学随着新的药物 (生物药物,JAK抑制剂) 和概念 (治疗目标,难以治疗的RA) 的发展而发展.
基因疗法,远程医疗和精准医学等进展越来越重要.
需要自动化的NLP方法来描述不断变化的研究环境.

研究的目的:

采用主题建模 (TM) 技术来识别过去23年的风湿病学研究中的关键主题和趋势.
分析大量的风湿病学摘要,以了解研究重点的转变.

主要方法:

来自PubMed的34个类风湿病学期刊 (2000-2023) 的96,004个摘要的回顾性分析.
使用BERTopicTM方法,它考虑语义关系和上下文.
选择了两个最佳模型,由风湿病学家手动标记主题,并使用线性回归识别了趋势.

主要成果:

确定了45-47个不同的主题,其中风湿性关节炎 (RA),全身性红斑狼和骨关节炎是最常见的.
动态TM揭示了COVID-19和Janus kinase (JAK) 抑制剂等新兴主题.
在脊髓手术和骨折中发现相关性增加,而抗脂综合征和败血性关节炎显示动量下降.

结论:

先进的NLP和TM有效地绘制了风湿病学研究格局,并确定了时间趋势.
该研究强调了风湿病学研究的动态性质,随着时间的推移,主题重点发生了重大转变.

关键词:

贝尔主题贝尔主题贝尔主题在PubMed上发现了这一点.人工智能的人工智能是人工智能.自然语言处理自然语言处理.主题建模主题建模变压器变压器趋势分析趋势分析

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