一个自然语言处理驱动的地图的衰老的研究景观
Jose Perez-Maletzki1,2, Jorge Sanz-Ros3
1Universidad Europea de Valencia, Faculty of Health Sciences, Department of Physiotherapy, Nutrition and Sports Sciences, Valencia 46010, España.
Aging
|November 26, 2025
概括
这项研究使用自然语言处理 (NLP) 和机器学习 (ML) 来绘制老龄化研究趋势. 研究结果显示,从基本生物学转向临床研究,特别是神经退行性疾病,老化生物学 (BoA) 和临床应用之间存在着显著的差距.
科学领域:
- 老年学和生物医学信息学
- 计算生物学 计算生物学
- 翻译医学是一种翻译医学.
背景情况:
- 衰老研究已经呈指数级增长,需要超越传统审查的先进合成工具.
- 老化文献的复杂性需要公正的,数据驱动的方法进行全面的分析.
- 了解衰老研究的演变和主题景观对于未来的方向至关重要.
研究的目的:
- 用先进的NLP和ML技术分析一个世纪以来老龄化研究的主题演变.
- 确定关键的研究集群,转变的优先事项,以及该领域的翻译差距.
- 提供一个可扩展的,数据驱动的替代传统叙事审查在衰老研究.
主要方法:
- 分析了1925年至2023年间发表的461,789篇关于衰老的研究摘要.
- 隐藏的迪里克莱特分配 (LDA),TF-IDF,缩小维度和集群算法的集成.
- 利用自然语言处理 (NLP) 和机器学习 (ML) 进行主题景观划分.
主要成果:
- 衰老研究的重点从早期的细胞/分子机制转向最近的临床研究,特别是神经退行性疾病.
- 在老化生物学 (BoA) 和临床研究领域之间存在着持久的概念差距.
- 发现不同的研究集群,包括可能被忽视的生物过程,并绘制相互连接的地图.
结论:
- 衰老研究重点已经转向临床应用,突出了翻译差距.
- NLP和ML提供了一种强大的,可扩展的方法来合成大量的科学文献.
- 确定的主题景观和相互联系可以指导未来的研究方向在老年学.
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