整合机器学习和统计方法来解决美国孕产妇医疗保健差异:系统性审查
Hala Al Sliti1, Ashaar Ismail Rasheed1, Saumya Tripathi2
1School of Systems Science and Industrial Engineering, Watson College of Engineering and Applied Science, SUNY Binghamton, Vestal, NY, United States.
International journal of medical informatics
|April 17, 2025
概括
机器学习 (ML) 和统计方法可以通过分析复杂的数据来帮助解决孕产妇健康差异. 需要进一步的研究来提高数据集的多样性和方法精度,以获得更好的结果.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 尽管有技术进步,但在种族,社会经济和地理方面,孕产妇健康差异仍然存在.
- 现有的医疗保健技术还没有充分解决这些重大公共卫生挑战.
- 需要先进的分析方法来解决持续存在的孕产妇健康不平等问题.
研究的目的:
- 审查机器学习 (ML) 和统计方法对识别孕产妇健康差异的影响.
- 评估这些方法在解决发现的差异方面的有效性.
- 建议未来的研究方向,以完善这些分析方法.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 在PubMed,Web of Science和ScienceDirect搜索了2012年1月至2024年2月的研究.
- 包括以美国为基地的,同行评审的研究,重点关注产后一年的孕产妇结果,并结合了孕产妇和婴儿健康数据.
主要成果:
- 147项研究符合纳入标准;129项使用了统计方法,18项使用了ML技术.
- 统计方法为政策和临床决策提供了可操作的见解.
- 机器学习探索了复杂的数据关系,但数据集多样性和方法精确性存在局限性.
- 大多数研究都集中在种族/社会经济差异上,较少的研究涉及地理层面.
结论:
- 机器学习和统计方法在改变孕产妇医疗保健方面具有重大潜力.
- 解决差异需要更广泛的数据集利用和改进的方法精度.
- 未来的研究应该优先考虑跨学科的合作,以提高预测的准确性和适用性.
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