使用基于自然语言处理的主题建模方法,追踪耐药性的研究主题和趋势
Mert Karabacak1, Pemla Jagtiani2, Ankita Jain3
1Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.
Epilepsia
|February 5, 2024
概括
药物耐药性 (DRE) 的研究是巨大的. 自然语言处理揭示了诸如大麻素和自身免疫性脑炎等关键主题,为这种神经系统疾病提供了有效的见解.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 图书统计学 图书统计学
背景情况:
- 影响全球超过7000万,其中30-40%的人患有耐药性 (DRE).
- DRE显著影响生活质量,并带来社会经济挑战.
- 对研究人员和临床医生来说,综合广泛的DRE文献是很困难的.
研究的目的:
- 使用自然语言处理 (NLP) 分析耐药性 (DRE) 出版景观.
- 确定DRE文献中的关键研究主题,趋势和新兴领域.
- 展示一种高效的文献综合和模式可视化方法.
主要方法:
- 使用Scopus数据库进行文档检索.
- 使用自然语言处理 (NLP) 与BERTopic建模,利用BERT (来自变压器的双向编码器表示).
- 分析了18个不同的话题,专注于10个最常见和新兴领域.
主要成果:
- 在DRE出版领域确定了18个不同的研究主题.
- 突出了包括抗药物 (AED),神经调节疗法和基因组学在内的常见话题.
- 揭示了"大麻二醇"",功能性大脑绘图"和"自身免疫性脑炎"是当前突出的话题.
结论:
- NLP主题建模为了解DRE研究环境提供了一种有效的方法.
- 这种方法揭示了不断变化的研究重点和对DRE兴趣的下降.
- 证明的方法可以应用于合成和可视化其他广泛的研究领域的模式.
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