与中风相关的期刊的趋势:使用主题建模检查出版模式
Burak Berksu Ozkara1, Mert Karabacak2, Konstantinos Margetis2
1Department of Neuroradiology, MD Anderson Cancer Center, 1400 Pressler Street, Houston, bX, 77030, USA.
概括
自然语言处理和主题建模可以有效地管理中风研究文献. 主要的主题包括动物模型和再注射疗法,并出现栓塞和葡萄糖代谢的新趋势.
科学领域:
- 神经科学和神经病学 神经科学和神经病学
- 生物医学信息学 生物医学信息学
背景情况:
- 脑卒中研究产生了大量的学术出版物.
- 有效地管理和解释这些文献对于推动该领域的发展至关重要.
- 自然语言处理 (NLP) 和主题建模提供了潜在的解决方案.
研究的目的:
- 证明NLP和主题建模在分析中风研究出版物的实用性.
- 为了加快文献审查,揭示隐藏的研究主题,并识别新兴趋势.
- 提供关于中风研究不断变化的景观的见解.
主要方法:
- 通过Scopus数据库,分析了来自五家主要中风期刊 (中风,IJOS,ESJ,TSR,JSCD) 的35,779篇文章.
- 提取标题,摘要,出版年份和引用次数.
- 应用BERTopic用于主题建模和线性回归用于使用Python 3.1识别趋势.
主要成果:
- 26,732 份文件被分为 30 个不同的主题.
- "动物模型"",康复"和"再输液疗法"成为最常见的主题.
- 热门话题包括"血栓塞","中枢髓和脑小板心脏病",以及"葡萄糖代谢".
结论:
- NLP和主题建模为理解研究演变和专业化提供了强大的方法.
- 研究结果强调了动物模型的重要性,康复研究的增长,以及反疗法的中心作用.
- 限制包括有限的期刊选择和对元数据质量的依赖.
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