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利用自然语言处理揭示创伤性脑损伤研究的主题和趋势

Mert Karabacak¹, Ankita Jain², Pemla Jagtiani³

¹Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.

Neurotrauma reports

|March 11, 2024

概括

本研究使用自然语言处理来分析自1990年以来的创伤性脑损伤 (TBI) 研究趋势. 基因组学和性激素正在成为热门话题,而创伤后睡眠是一个冷却区域.

科学领域:

神经科学和神经病学神经科学和神经病学
医疗信息学医疗信息学

背景情况:

创伤性脑损伤 (TBI) 研究已经显著扩大,需要了解不断变化的趋势.
确定研究重点的转变对于指导未来的科学调查和资源分配至关重要.

研究的目的:

确定从1990年起的TBI研究中占主导地位的主题.
使用先进的NLP技术,划分"热" (兴趣增加) 和"冷" (兴趣减少) 的研究领域.
具体分析当前十年 (2020年代) 的TBI研究趋势.

主要方法:

利用 BERTopic,一种先进的自然语言处理 (NLP) 技术,分析了 42,422 篇 TBI 研究文章.
从文章中提取关键主题和独特的关键词,以代表核心研究主题.
在话题概率上使用线性回归模型来检测随时间推移的趋势,识别获得和失去相关性.

主要成果:

确定了27个不同的主题,其中"康复"",TBI的分子机制"和"脑震荡"是最常见的.
2020年代最热门的话题包括"基因组学"",性激素"和"扩散张力图像".
确定了"创伤后睡眠"",感官功能"和"高度疗法"作为冷却研究领域.

结论:

创伤伤害研究是动态的,重点转移到特定的主题上.
发现可以引导研究人员进入新兴领域,并确定未被充分探索的研究问题.
NLP和机器学习为合成文献提供了强大的工具,并为TBI和其他医学领域的未来研究方向提供了信息.

关键词:

这是一个TBI,TBI.这是一个热门话题.自然语言处理自然语言处理.研究趋势研究趋势主题建模主题建模创伤性脑损伤是一种创伤性脑损伤

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