机器学习辅助的文献选,用于与药物使用过程相关的系统审查
Michelle Cawley1, Rebecca Carlson1, Tyler A Vest2
1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
概括
用外部数据训练的机器学习 (ML) 模型可以有效地选文章以检查药物使用过程 (MUP) 和门诊护理 MUP (ACMUP) 评论. 这种方法节省了大量的时间,因为它排除了不相关的研究,同时保持了大量的相关文献回忆.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 证据综合 证据综合
背景情况:
- 对于药物使用过程 (MUP) 和门诊护理 MUP (ACMUP) 等年度系列的文献评论产生了广泛的数据集.
- 有效选大量文献对于及时综合证据至关重要.
- 传统的手动查可能是耗时和资源密集的.
研究的目的:
- 引入和评估一种使用机器学习 (ML) 的新方法,用于协助MUP和ACMUP审查中的文章选.
- 为了证明训练ML模型与外部数据集的有效性,以预测文章相关性.
- 通过排除不相关的搜索结果来减少文献评论中的手工工作量.
主要方法:
- 开发和应用在外部数据集上训练的ML算法,以预测文章的相关性.
- 从MUP和ACMUP审查系列中利用过去的选决策作为培训数据.
- 模拟了ML模型的性能与已知的手动选决策对比,以评估准确性和时间节省.
主要成果:
- 在192项相关研究中,ML方法正确识别了187项.
- 在涉及17227项独特研究的模拟中,ML使得13201项研究在没有手动选的情况下被排除在外.
- 在模拟中保持了95%或更高的相关物品召回率.
结论:
- 基于ML的文章选方法是有效的,适用于系统审查和持续审查系列.
- 这种方法在MUP和其他药房实践学科的证据合成过程中大大节省了时间.
- 促进证据综合的更快速发布,支持药剂师提高护理效率和减少药物错误.
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