机器学习辅助的健康经济学和政策审查:一个比较评估
Ludovico Cavallaro1, Vittoria Ardito1, Michael Drummond1,2
1Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Via Sarfatti, 10, 20136, Milan, MI, Italy.
Applied health economics and health policy
|March 28, 2025
概括
机器学习工具可以通过提高标题和摘要选效率,显著加快卫生经济学和政策方面的文献审查. 这项技术显示出高可靠性,可能节省相当多的审查员时间.
科学领域:
- 卫生经济学 卫生经济学
- 政策分析 政策分析
- 图书统计学 图书统计学
背景情况:
- 科学文献的指数增长给在健康经济学和政策领域进行全面的文献审查带来了重大挑战.
- 有效选标题和摘要对于管理研究量和确保证据综合的质量至关重要.
研究的目的:
- 评估机器学习 (ML) 工具在提高卫生经济学和政策文献审查的标题和摘要选过程中的有效性和可靠性.
- 评估ML工具的潜力,以简化系统审查和提高审查员效率.
主要方法:
- 在"模拟模式"中使用了机器学习工具ASReview,以评估其在从10,246个独特记录的数据集中识别相关记录 (RRF) 的性能.
- 通过采样和启发式停止标准,在不同先前知识 (5,10,15条记录) 的场景中评估性能,每场景进行100次模拟.
主要成果:
- 在选了仅25%的样本后,ML工具实现了97%的RRF中位数,这表明大大节省了时间 (约. 32个工作日).
- 虽然更高的先验知识提高了初始性能,但在整个选过程中,ML工具的准确性保持稳健.
- 启发式标准显示了可比的RRF中位数,但由于过早停止,表现出更大的变化.
结论:
- 机器学习工具为提高卫生经济学和政策研究中标题和抽象选效率提供了一个有希望的解决方案.
- 制定明确的ML辅助审查指南对于保持严格的证据质量标准和确保系统审查的可靠性至关重要.
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