机器学习用于传染病控制:一个范围审查
Sharon Birdi1, Atushi Patel1, Roxana Rabet1
1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
Public health reviews
|March 2, 2026
概括
机器学习 (ML) 越来越多地用于传染病控制,特别是在COVID-19中. 未来的研究必须解决ML模型中的算法偏差,以确保可靠和公平的公共卫生结果.
科学领域:
- 公共卫生 公共卫生
- 传染病流行病学 传染病流行病学
- 机器学习应用 机器学习应用
背景情况:
- 传染病对全球健康构成重大风险,以COVID-19大流行为例.
- 通过机器学习 (ML) 快速分析数据对于有效的疫情检测和控制至关重要.
研究的目的:
- 系统地审查 ML 在传染病控制中的应用,以保护公共健康.
- 调查ML模型中的算法偏差,并探索缓解策略.
主要方法:
- 在多个数据库 (MEDLINE,Embase,Scopus等) 中进行全面的文献搜索. 从2000年开始到2022年.
- 包括同行评审的研究,将ML模型应用于高流行性传染病.
主要成果:
- 209项研究符合纳入标准,自2020年以来,ML应用的激增,特别是针对SARS-CoV-2.
- ML的使用扩展到疟疾,艾滋病毒和结核病等疾病.
- 只有8.61%的研究解决了偏见,更少的研究实施了缓解策略.
结论:
- 机器学习在传染病的疾病监测中越来越有用.
- 在ML模型设计中优先考虑偏差缓解对于提高公共卫生中的可靠性和公平性至关重要.
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