一个深度神经网络模型,用于将药房实践出版物分类为研究领域
Samuel O Adeosun1, Afua B Faibille2, Aisha N Qadir2
1Department of Clinical Sciences, High Point University One University Parkway, High Point, NC, 27268, USA; Fred Wilson School of Pharmacy, High Point University One University Parkway, High Point, NC, 27268, USA.
Research in social & administrative pharmacy : RSAP
|November 10, 2024
概括
一个新的药学实践研究领域分类器 (PPRDC) 准确地将教师出版物分为四个领域,在文献分析中表现优于通用大型语言模型 (gpLLMs).
科学领域:
- 图书统计学 图书统计学
- 药房实践研究研究 药房实践研究
- 在学术界的人工智能.
背景情况:
- 药房实践研究包括临床,社会和行政领域,但缺乏标准化的分类.
- 格拉纳达声明强调了在定义研究领域方面需要达成共识的必要性.
- 提出了四个关键领域:临床,教育,社会和行政,以及基本和翻译.
研究的目的:
- 开发一种机器学习分类器,将药房实践学院的出版物分类为四个定义的研究领域.
- 评估分类器的性能与最先进的通用大型语言模型 (gpLLMs) 相比,使用零射击方法.
主要方法:
- 一个来自变压器的双向编码器表示 (BERT) 模型使用1000篇 (2018-2021) 来自药房实践学院出版物的摘要进行了微调.
- 开发的模型,药学实践研究领域分类器 (PPRDC),与7个领先的gpLLM对80个随机选择的摘要 (2023) 进行了比较.
- 使用F1,回忆力,精度,准确性和可重现性的科恩卡帕来评估性能.
主要成果:
- 美国人民民主民主共和国取得了高5倍交叉验证指标:89.4%F1,90.2%回忆,89.0%精度和95.5%准确度.
- 在零射击分类中,PPRDC表现出完美的可重现性 (科恩卡帕=1.0),并且在零射击分类中显著超过所有测试的gpLLM.
- 针对PPRDC的具体F1分数是:教育 (96.2%),临床 (92.7%),社会 (85.8%) 和翻译 (83.1%).
结论:
- 与gpLLM相比,PPRDC在分类药房实践研究摘要方面提供了更高的性能.
- 该工具促进了文献计量研究,并通过帮助作者和编辑在出版相关决策中,支持格拉纳达声明的目标.
- 在药房实践学术研究的系统分析和分类方面,PPRDC代表了一项重大进展.
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