医疗经济评估中的机器学习:范围审查协议
Hanan Daghash1,2, Ashleigh Kernohan1, Rosiered Brownson-Smith2
1Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.
本范围审查探讨了机器学习 (ML) 在健康经济评估中的应用. 它确定了将ML纳入该领域的挑战和障碍,旨在提高理解并指导未来的研究.
科学领域:
- 卫生经济学 卫生经济学
- 人工智能的人工智能
- 医疗保健服务研究 医疗服务研究
背景情况:
- 机器学习 (ML) 应用程序正在迅速发展,显示出改变医疗保健的巨大潜力.
- 然而,将ML纳入健康经济评估仍未得到充分探索,并带来了独特的挑战.
研究的目的:
- 在健康经济评估中对ML的应用进行范围审查.
- 确定与在健康经济评估中使用ML相关的潜在挑战和障碍.
主要方法:
- 使用PRISMA-ScR (系统性审查和元分析的首选报告项目,范围审查的扩展) 方法.
- 在MEDLINE (Ovid),Embase (Ovid),IEEE Xplore和Cochrane图书馆数据库中进行了全面的搜索.
- 适用研究类型,数据源,方法和结果 (SDMO) 框架的资格标准.
主要成果:
- 最初的搜索结果为 4141 条记录,其中 3718 条记录被选为标题和摘要.
- 已获取30份报告进行详细的资格评估.
- 数据提取和绘制正在进行中,预计结果将于2025年底发布.
结论:
- 这一审查将有助于更好地了解ML在健康经济评估中的整合.
- 它旨在阐明阻碍在这个领域采用ML的障碍和挑战.
- 结果将为未来的研究和在健康经济学中对ML的实际实施策略提供信息.
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