机器学习和公共卫生政策评估:研究动态和挑战前景
Zhengyin Li1, Hui Zhou1, Zhen Xu1
1Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, China.
Frontiers in public health
|February 14, 2025
概括
机器学习提供了先进的公共卫生政策评估,但数据偏见和隐私等挑战仍然存在. 提出了创新策略,以提高大数据环境中的准确性和公平性.
科学领域:
- 公共卫生 公共卫生
- 卫生政策评估 卫生政策评估
- 机器学习应用 机器学习应用
背景情况:
- 公共卫生政策评估对于健康结果和资源分配至关重要.
- 大数据给传统的评估方法带来了新的挑战,需要创新的方法.
研究的目的:
- 审查传统的公共卫生政策评估方法.
- 在政策评估中探索机器学习 (ML) 应用.
- 解决挑战,并在大数据环境中为ML提出解决方案.
主要方法:
- 关于传统评估原则和局限性的文献综述.
- 分析ML应用步骤和政策评估中的实际例子.
- 讨论包括解释性,偏见,不公平和隐私在内的挑战.
主要成果:
- ML显示了更精确的公共卫生政策评估的潜力.
- 确定了关键挑战:模型可解释性,数据偏差,健康不平等的延续和数据隐私.
- ML的承诺受到重大伦理和技术障碍的抑制.
结论:
- 整合数据驱动和理论驱动的方法,以获得更好的解释性.
- 采用多层次数据策略来减少偏见和健康不平等.
- 通过保障和法律框架确保数据隐私;使用验证来确保可靠性.
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