机器学习和人工智能用于传染病监测,诊断和预后
Brandon C J Cheah1, Creuza Rachel Vicente2, Kuan Rong Chan1
1Program in Emerging Infectious Diseases, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore.
Viruses
|July 30, 2025
概括
人工智能 (AI) 和机器学习 (ML) 显示出传染病管理的前景. 本综述确定了适用于监测,诊断和预后的AI和ML模型,并建议可解释AI和组合学习用于临床整合.
科学领域:
- 计算生物学是一种计算生物学.
- 流行病学 流行病学
- 医疗信息学医学信息学
背景情况:
- 大数据,数字表型和公共数据集使AI/ML能够在传染病管理中发挥作用.
- 目前的审查缺乏范围,阻碍了对临床实践的最佳AI/ML模型选择.
- 人工智能和机器学习为分析传染病中复杂的临床和分子数据提供了强大的工具.
研究的目的:
- 进行范围的文献审查,确定与传染病管理相关的ML模型和应用.
- 为在临床实践中实施ML模型提出可操作的工作流程.
- 确定最适合用于传染病监测,诊断和预后的AI/ML模型.
主要方法:
- 在PubMed,谷歌学者和ScienceDirect (2020年1月至2024年4月) 上进行文献搜索.
- 关键词:AI,ML,公共卫生,监测,诊断,预后,传染病.
- 包括77项专注于监测,预后和诊断的研究;排除了缺乏公共数据集或ML模型描述的研究.
主要成果:
- 传染病管理中的不同类型的数据需要不同的AI/ML模型来实现最佳性能.
- 可解释的人工智能和集体学习模型展示了广泛的适用性和高预测准确性.
- 大多数审查的研究缺乏跨多元群体的验证,限制了概括性.
结论:
- 可解释的人工智能和集体学习模型有望提高传染病监测,诊断和预后.
- 将这些ML模型集成到临床工作流中可以增强决策.
- 进一步在不同人群中的验证对于AI/ML在传染病中的广泛临床采用至关重要.
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