在头痛研究中,什么能预测引用数量和翻译影响? 一个机器学习分析分析
Antonios Danelakis1,2, Helge Langseth1,2, Parashkev Nachev3
1NorHead Norwegian Centre for Headache Research, Trondheim, Norway.
概括
机器学习模型可以预测引用数量和头痛研究中的翻译影响. 文献计量数据最好预测引用,而内容分析与文献计量结合预测翻译影响.
科学领域:
- 图书统计学 图书统计学
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 开发科学研究影响的预测模型至关重要.
- 之前的研究没有使用机器学习来预测引用数和头痛研究中的翻译影响.
研究的目的:
- 创建第一个机器学习模型来预测引用数量和头痛研究的翻译影响.
- 确定影响这些结果的关键因素.
主要方法:
- 利用了来自三个头痛期刊的8600个出版物的文献计量数据和出版物内容 (标题,摘要,关键词).
- 实施机器学习模型来预测5年引用数区间和翻译影响.
- 使用样本外面面积在接收器操作特征曲线 (AUC) 下评估模型性能.
主要成果:
- 一个渐变增强模型实现了0.81的AUC,用于预测引用数类.
- 诸如页数,参考文献和作者h-index等图书识别因素是重要的预测因素.
- 一个随机森林模型,使用文献计量和内容数据,实现了0.71的AUC来预测转化影响.
结论:
- 图书统计数据有效地预测了头痛研究中的引用数量.
- 将文献计量数据与出版物内容结合起来,可以更好地预测翻译影响.
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