自然语言处理揭示了两十年来脊柱杂志的研究趋势和主题:一个主题建模研究研究
Mert Karabacak1, Konstantinos Margetis1
1Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Ave, New York, NY, 10029 USA.
概括
这项研究使用自然语言处理来分析脊柱研究趋势,确定"退行性宫肌病"和"螺丝生物力学"作为关键的演变话题. 这些发现揭示了脊柱文学的动态景观.
科学领域:
- 脊柱研究 脊柱研究
- 医学文献分析 医学文献分析
- 自然语言处理自然语言处理.
背景情况:
- 脊柱研究领域正在迅速发展,不断出现新的话题.
- 分析文献趋势提供了对不断变化的研究格局的见解.
研究的目的:
- 在脊柱杂志中识别流行和新兴的研究主题和趋势.
- 利用自然语言处理 (NLP) 和主题建模进行分析.
主要方法:
- 采用BERTopic,一种NLP技术,分析来自"脊柱杂志"的文章.
- 通过关键词集群和代表性文件确定主题.
- 使用线性回归来追踪随时间推移的主题趋势 (热/冷主题).
主要成果:
- 将3358份文件分类为30个不同的主题.
- 最常见的主题:"结果测量"",脊椎病"",内腔损伤".
- 最热门的历史话题:"退行性宫肌病"",骨质疏松症"",阿片类药物使用".
- 当前十年最热门的话题:"螺丝生物力学"",脊柱肌肉"",生物学的融合".
结论:
- 脊柱研究是动态的,随着时间的推移,重点变化.
- 洞察力可以指导未来的研究,政策,并确定新兴领域.
- NLP提供了强大的方法来分析广的学术文献.
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