机器学习应用中的偏见,以解决人口层面的非传染性疾病:范围审查
Sharon Birdi1, Roxana Rabet1, Steve Durant1
1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
BMC public health
|December 28, 2024
概括
机器学习 (ML) 在人口健康中越来越多地用于非传染性疾病 (NCD). 本综述强调了ML的应用,潜在的偏见和缓解策略,以实现公平的公共卫生结果.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 是人口和公共卫生领域日益增长的工具,有助于流行病学研究,监测和卫生计划评估.
- 本范围审查特别关注在人口健康研究中对非传染性疾病 (NCD) 的ML的应用.
- 该审查还调查了ML模型和用于减轻它们的策略中的潜在算法偏差.
研究的目的:
- 进行范围审查,确定在人口健康中使用ML的研究,特别强调非传染性疾病.
- 检查研究的非传染性疾病类型,数据源,技术方法和报告的算法偏差.
- 评估偏见缓解策略在文献中所涉及的程度.
主要方法:
- 在多个数据库中进行了全面的文献搜索,包括Medline,Embase,Cochrane,CINAHL,Scopus,ACM数字图书馆,Inspec和Web of Science.
- 该搜索涵盖了截至2022年3月的同行评审,索引文献.
- 包括的研究被分析为他们的目标 (算法比较与疾病建模),非传染性疾病类型,数据来源,技术方法,偏见和管辖权.
主要成果:
- 在 27,310 项已识别的研究中,有 65 项被纳入了审查.
- 大多数研究 (80%) 专注于为人口健康输出的疾病建模,而20%涉及算法比较.
- 2型糖尿病是最常被研究的非传染性疾病,ML主要用于风险建模. 偏见缓解没有得到广泛的解决,重点是与性别相关的偏见.
结论:
- 本综述综合了目前在非传染性疾病中的ML应用,强调了潜在的偏见和缓解努力.
- 未来的研究应该扩展到传染病,并探索低收入和中等收入国家ML模型的可转移性.
- 结果旨在为公平有效地使用ML提供指导方针,以提高人口健康结果.
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