机器学习用于研究慢性疾病的风险因素:一个范围审查
Mahek Shergill1,2, Steve Durant1, Sharon Birdi1
1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.
概括
机器学习 (ML) 在研究慢性疾病风险因素方面表现有前途. 然而,很少有研究有效地解决了算法偏差问题,突出了在人口健康研究中需要改进方法的需求.
科学领域:
- 人口健康 人口健康
- 公共卫生 公共卫生
- 计算流行病学计算流行病学
背景情况:
- 机器学习 (ML) 为健康研究中分析大型数据集提供了先进的功能.
- 慢性疾病是主要的公共卫生问题,需要创新的方法来了解它们的风险因素.
- 在ML模型中的算法偏差可以加剧健康差异,需要在应用中仔细考虑.
研究的目的:
- 进行使用ML研究的研究范围审查,以检查人口层面的慢性疾病风险因素.
- 为了确定ML应用程序,重点关注关键的风险因素:烟草使用,酒精消费,饮食,体力活动和压力.
- 评估研究是否包含了减轻算法偏差的方法.
主要方法:
- 在主要的科学数据库 (Medline,Embase,Cochrane,Scopus,ACM,INSPEC,Web of Science) 中进行了全面的文献搜索.
- 包括的研究被分析,以确定是否考虑算法偏差,并确定缓解策略.
- 审查的重点是与常见的慢性疾病风险因素相关的ML应用.
主要成果:
- 在10329项已识别的研究中,有20项符合审查的纳入标准.
- ML被用于各种目的,包括疾病预测,数据分类和识别新型风险因素与疾病的关联.
- 在45%的纳入研究中讨论了算法偏差,社会人口统计学变量通常用于增强模型性能,而不是解决偏差.
结论:
- 本综述提供了关于ML在人口健康和公共卫生研究中的应用的见解.
- 有必要开发和实施强大的方法来缓解用于慢性疾病研究的ML模型中的算法偏差.
- 需要进一步的研究,以确保ML应用促进健康公平,避免延续差异.
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