在牛皮文献中识别研究热点和趋势:使用代理商自动调整主题建模
Sunsi Wu1, Dan Wang2, Xinpei Gu3
1Department of Dermatology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
The Journal of investigative dermatology
|February 2, 2025
概括
人工智能框架AgenTopic分析了从2000年到2023年的牛皮研究趋势. 它确定了理解疾病病原和治疗开发的关键转变,超越了传统方法.
科学领域:
- 皮肤病学 皮肤病学
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 牛皮的研究正在迅速扩大,使趋势分析具有挑战性.
- 现有的方法与庞大的医学文献的复杂性作斗争.
研究的目的:
- 开发和验证AgenTopic,这是一个交互式主题建模框架,用于分析牛皮研究文献.
- 从2000年到2023年,确定牛皮的关键研究趋势和模式.
主要方法:
- 使用了来自变压器的双向编码器表示 (BERT) 嵌入,缩小维度和集群.
- 集成了一个语言模型反循环和支持向量回归-趋势分析的线性模型.
- 将框架应用于2000年至2023年间发表的PubMed文章.
主要成果:
- AgenTopic在8个类别中提取了158个与牛皮相关的主题,超越了传统方法.
- 在研究类别中确定了非线性研究增长模式 (R2 = 0.75-0.97).
- 突出趋势:指甲牛皮,脊椎关节炎,IL-17通路聚焦,生物制剂/小分子抑制剂和并发症.
结论:
- AgenTopic提供了一种有效的AI驱动方法来分析复杂的皮肤病学文献.
- 该框架在识别研究趋势方面取得了与专家评价相提并论的表现.
- 展示了人工智能在推进医学文献分析和趋势发现方面的潜力.
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