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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

130
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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人工智能对严重登革热早期预警系统的方法

Dina Nur Anggraini Ningrum1, Yu-Chuan Jack Li2,3,4, Chien-Yeh Hsu5,6

  • 1Public Health Department, Universitas Negeri Semarang, Semarang City, Indonesia.

Studies in health technology and informatics
|January 25, 2024
PubMed
概括

人工智能 (AI) 模型使用时空数据准确预测登革热爆发和病例,提前一周. 这种人工智能方法增强了早期预警系统,以实现有效的基于社区的载体控制.

关键词:
人工智能的人工智能是人工智能.登革热发病率病例预测 预测登革热爆发的预测和预测预警系统的早期预警系统.

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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

背景情况:

  • 登革热是一个重大的全球健康负担,每年导致数百万感染和数千人死亡,主要是儿童.
  • 有效的载体控制对于预防登革热至关重要,因为疫苗有局限性.
  • 早期检测和医疗接入可以大大降低登革热死亡率.

研究的目的:

  • 开发一种具有时空方法的人工智能 (AI) 模型,用于预测登革热爆发和发病情况.
  • 创建一个预测模型,准备在预警系统应用中实施.
  • 改善登革热监测,并为印尼塞马兰格市等特有地区的社区传播媒介控制战略提供信息.

主要方法:

  • 利用了来自印度尼西亚塞马兰市 (2014年1月 - 2021年12月) 的时空,气象,气候和登革热监测数据.
  • 采用机器学习和长短期记忆 (LSTM) 网络进行预测建模,将数据分为80%的训练和20%的测试集.
  • 使用准确度,AUROC,精度,回忆和F1得分评估了疫情预测;使用MSE,MAE,RMSE和R平方评估了发病率预测.

主要成果:

  • 额外树木分类器模型在登革热爆发预测方面取得了很高的表现 (准确率:0.8925,AUROC:0.9529,F1得分:0.7238).
  • CatBoost回归模型在登革热发病例预测方面表现优异 (R平方:0.5621,RMSE:1.0891).
  • 结合时空空间数据的AI模型显著提高了登革热爆发和发病情况的预测准确性.

结论:

  • 人工智能,特别是以时空方法的方法,为预测登革热爆发和发病例提供了一个强大的工具.
  • 开发的AI模型适合整合到预警系统中,从而能够及时干预.
  • 实施人工智能驱动的早期预警系统可以加强决策者和社区参与针对性,基于社区的载体控制工作.