整合可解释的人工智能和One Health:在打击传染病方面的新前沿
Yanni Cao1, Emma Lancaster1, Jiyoung Lee2
1Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA.
EBioMedicine
|March 13, 2026
概括
与"一健康"方法集成的可解释AI (XAI) 增强了传染病 (ID) 智能,以更好地预测和控制. 这种协同作用有助于识别疫情驱动因素并优化公共卫生战略.
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
- 人工智能在医学中的应用
- 一个健康倡议一项健康倡议
背景情况:
- 传染病 (IDs) 构成重大全球健康威胁,许多新出现的IDs是动物性传染病,受到环境因素的影响.
- 目前用于ID预测的机器学习模型往往缺乏有效公共卫生干预所需的可解释性.
- 一个健康的方法对于解决复杂的,相互关联的健康挑战至关重要.
研究的目的:
- 提出可解释AI (XAI) 作为One Health系统的核心组成部分,以提高传染病情报.
- 探索XAI在改善ID监控,预测和响应方面的潜力.
- 确定在开发XAI支持的One Health框架中的关键挑战和机遇.
主要方法:
- 概念框架将可解释AI (XAI) 整合到一个健康模式中.
- 审查XAI在传染病情报中的新兴应用.
- 讨论与数据,治理,隐私和公平相关的挑战.
主要成果:
- XAI可以为复杂的机器学习模型提供可解释性,使预测的归因和关键爆发驱动因素的识别成为可能.
- 新兴应用包括改进的动物传染病溢出监测,抗菌素耐药性监测和资源配置优化.
- 将XAI集成到One Health系统中,为传染病情报提供了一个新的组织原则.
结论:
- 将XAI嵌入到One Health框架中,为传染病情报和管理提供了一个强大的新战略.
- 解决数据协调,治理,隐私和公平利益分配方面的挑战对于成功实施至关重要.
- 需要跨部门的合作和方法的创新来推进XAI支持的One Health系统.
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