感染监测中的人工智能:数据集成,应用和未来方向
Jin-Hua Li1, Yi-Ju Tseng2, Shu-Hui Chen3
1Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan.
Biomedical journal
|November 8, 2025
概括
人工智能 (AI) 通过整合各种数据来提前检测和预测疫情,增强了感染监测. 人工智能模型改进了公共卫生策略,但需要解决数据隐私和验证挑战.
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
- 计算流行病学计算流行病学
背景情况:
- 传统的感染监测方法的反应时间缓慢,分析能力有限.
- 整合不同的数据源对于及时和准确的疾病监测至关重要.
- 人工智能 (AI) 提供先进的分析能力来克服这些局限性.
研究的目的:
- 审查人工智能在感染监测中的变革潜力.
- 分析AI处理异质数据集的能力,以改善疾病检测和预测.
- 在公共卫生监测中综合特定的AI应用.
主要方法:
- 对同行评审的文章和报告进行叙述性审查.
- 对人工智能的机器学习能力进行分析,以处理各种数据 (EHR,社交媒体,时空,可穿戴设备).
- 人工智能应用在疫情预测,实时监测和传播跟踪方面的综合.
主要成果:
- 由人工智能驱动的模型改善了疫情预测和疾病发病率估计.
- 人工智能通过识别易感人群和地理热点来提高风险评估.
- 特定应用程序,如用于流感预测的社交媒体和用于实时监测的可穿戴设备,显示出有希望.
结论:
- 人工智能显著提高了感染监测,使得早期检测和改善公共卫生战略.
- 挑战包括数据隐私,模型验证,以及对多样化测试的需求.
- 未来的工作应侧重于人工智能模型的适应性,强大的验证和数据集成,以进行有效的干预.
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