人工智能驱动的关联洞察力:基于LLM的欧洲医学信息学会议的图书统计研究
Ehsan Bitaraf1, Maryam Jafarpour2
1Rajaei Cardiovascular Research Institute, Iran University of Medical Sciences, Tehran, Iran.
Studies in health technology and informatics
|May 17, 2025
概括
大型语言模型 (LLM) 通过增强附属性提取,显著改善了欧洲医疗信息学的文献识别分析. 这揭示了欧洲的主要研究趋势,作者影响和合作网络.
科学领域:
- 图书统计学 图书统计学
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 传统的文献计量分析在数据准确性和标准化方面面临挑战.
- 分析研究趋势需要强大的方法来提取出版物和协作数据.
研究的目的:
- 评估大型语言模型 (LLM) 在加强欧洲医疗信息学会议的文献计量分析方面的有效性.
- 从1996年到2024年,确定出版量,作者影响和机构合作的趋势.
- 使用基于LLM的技术来提高归属提取的准确性.
主要方法:
- 分析了欧洲医学信息学会议 (1996-2024) 的图书统计数据.
- 传统的文献计量方法得到了基于大型语言模型 (LLM) 的技术的增强.
- 关联提取精度是评估LLM绩效的关键指标.
主要成果:
- 与传统方法相比,基于LLM的技术显著提高了归属提取精度.
- 分析显示了欧洲各机构出版成果,作者影响力和合作模式的明显趋势.
- 领先的贡献者,协作网络和卓越的地理中心被确定并可视化.
结论:
- 大型语言模型提供了一个强大的工具,用于推进医学信息学中的文献计量分析.
- 法律学士提供了对研究动态和协作结构的更深入的见解.
- 解决数据标准化和计算资源挑战对于在文献计量学中更广泛地采用LLM至关重要.
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